1 computational tools for linguists inderjeet mani georgetown university [email protected]

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Computational Tools for Linguists

Inderjeet Mani

Georgetown University

[email protected]

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Topics

Computational tools for

- manual and automatic annotation of linguistic data

- exploration of linguistic hypotheses

Case studies

Demonstrations and training

Inter-annotator reliability

Effectiveness of annotation scheme

Costs and tradeoffs in corpus preparation

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Outline

Topics

- Concordances

- Data sparseness

- Chomsky’s Critique

- Ngrams

- Mutual Information

- Part-of-speech tagging

- Annotation Issues

- Inter-Annotator Reliability

- Named Entity Tagging

- Relationship Tagging

Case Studies

- metonymy

- adjective ordering

- Discourse markers: then

- TimeML

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Corpus Linguistics

Use of linguistic data from corpora to test linguistic hypotheses => emphasizes language use

Uses computers to do the searching and counting from on-line material

- Faster than doing it by hand! Check?

Most typical tool is a concordancer, but there are many others!

Tools can analyze a certain amount, rest is left to human!

Corpus Linguistics is also a particular approach to linguistics, namely an empiricist approach

- Sometimes (extreme view) opposed to the rationalist approach, at other times (more moderate view) viewed as complementary to it

- Cf. Theoretical vs. Applied Linguistics

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Empirical Approaches in Computational Linguistics

Empiricism – the doctrine that knowledge is derived from experience

Rationalism: the doctrine that knowledge is derived from reason

Computational Linguistics is, by necessity, focused on ‘performance’, in that naturally occurring linguistic data has to be processed

- Naturally occurring data is messy! This means we have to process data characterized by false starts, hesitations, elliptical sentences, long and complex sentences, input that is in a complex format, etc.

The methodology used is corpus-based

- linguistic analysis (phonological, morphological, syntactic, semantic, etc.) carried out on a fairly large scale

- rules are derived by humans or machines from looking at phenomena in situ (with statistics playing an important role)

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Example: metonymy

Metonymy: substituting the name of one referent for another

- George W. Bush invaded Iraq

- A Mercedes rear-ended me

Is metonymy involving institutions as agents more common in print news than in fiction?

- “The X Vreporting”

Let’s start with: “The X said”

- This pattern will provide a “handle” to identify the data

Exploring Corpora

Datasetshttp://complingtwo.georgetown.edu/cgi-bin/gwilson/bin/

DataSets.cgi

Metonymy Test using Corporahttp://complingtwo.georgetown.edu/~gwilson/Tools/Metonymy/

TheXSaid_MST.html

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‘The X said’ from Concordance data

Words Freq Freq/

M Words

Fiction 1870 1.7M 60 35

Fiction 2000 1.5M 219 146

Print News 1.9M 915 481

The preference for metonymy in print news arises because of the need to communicate Information from companies and governments.

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Chomsky’s Critique of Corpus-Based Methods

1. Corpora model performance, while linguistics is aimed at the explanation of competence

If you define linguistics that way, linguistic theories will never be able to deal with actual, messy data

Many linguists don’t find the competence-performance distinction to be clear-cut. Sociolinguists have argued that the variability of linguistic performance is systematic, predictable, and meaningful to speakers of a language.

Grammatical theories vary in where they draw the line between competence and performance, with some grammars (such as Halliday’s Systemic Grammar) organized as systems of functionally-oriented choices.

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Chomsky’s Critique (concluded)

2. Natural language is in principle infinite, whereas corpora are finite, so many examples will be missed

Excellent point, which needs to be understood by anyone working with a corpus.

But does that mean corpora are useless? Introspection is unreliable (prone to performance factors,

cf. only short sentences), and pretty useless with child data.

Also, insights from a corpus might lead to generalization/induction beyond the corpus– if the corpus is a good sample of the “text population”

3. Ungrammatical examples won’t be available in a corpusDepends on the corpus, e.g., spontaneous speech, language

learners, etc.The notion of grammaticality is not that clear

- Who did you see [pictures/?a picture/??his picture/*John’s picture] of?

- ARG/ADJUNCT example

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Which Words are the Most Frequent?

Common Words in Tom Sawyer (71,730 words), from Manning & Schutze p.21

Will these counts hold in a different corpus (and genre, cf. Tom)? What happens if you have 8-9M words? (check usage demo!)

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Data Sparseness

Many low-frequency words

Fewer high-frequency words.

Only a few words will have lots of examples.

About 50% of word types occur only once

Over 90% occur 10 times or less.

So, there is merit to Chomsky’s 2nd objection

Word Frequency Number of words of that frequency

1 3993

2 1292

3 664

4 410

5 243

6 199

7 172

8 131

9 82

10 91

11-50 540

51-100 99

>100 102

Frequency of word types in Tom Sawyer, from M&S 22.

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Zipf’s Law: Frequency is inversely proportional to rank

Word Freq f Rank r f.r the 3332 1 3332

and 2972 2 5944

a 1775 3 5325

he 877 10 8770

but 410 20 8200

be 294 30 8820

there 222 40 8880

one 172 50 8600

about 158 60 9480

more 138 70 9660

never 124 80 9920

oh 116 90 10440

two 104 100 10400

turned 51 200 10200

you’ll 30 300 9000

name 21 400 8400

comes 16 500 8000

group 13 600 7800

lead 11 700 7700

friends 10 800 8000

begin 9 900 8100

family 8 1000 8000

brushed 4 2000 8000

sins 2 3000 6000

could 2 4000 8000

applausive 1 8000 8000

Empirical evaluation of Zipf’s Law on Tom Sawyer, from M&S 23.

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Illustration of Zipf’s Law (Brown Corpus, from M&S p. 30)

logarithmicscale

See also http://www.georgetown.edu/faculty/wilsong/IR/WordDist.html

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Tokenizing words for corpus analysis

1. Break on - Spaces? 犬に当る男の子は私の兄弟である。

inuo butta otokonokowa otooto da

- Periods? (U.K. Products)- Hyphens? data-base = database = data base- Apostrophes? won’t, couldn’t, O’Riley, car’s

2. should different word forms be counted as distinct?- Lemma: a set of lexical forms having the same stem,

the same pos, and the same word-sense. So, cat and cats are the same lemma.

- Sometimes, words are lemmatized by stemming, other times by morphological analysis, using a dictionary and/or morphological rules

3. fold case or not (usually folded)?- The the THE Mark versus mark- One may need, however, to regenerate the original

case when presenting it to the user

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Counting: Word Tokens vs Word Types

Word tokens in Tom Sawyer: 71,370

Word types: (i.e., how many different words) 8,018

In newswire text of that number of tokens, you would have 11,000 word types. Perhaps because Tom Sawyer is written in a simple style.

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Inspecting word frequencies in a corpus

http://complingtwo.georgetown.edu/cgi-bin/gwilson/bin/DataSets.cgi

Usage demo:

- http://complingtwo.georgetown.edu/cgi-bin/gwilson/bin/Usage.cgi

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Ngrams

Sequences of linguistic items of length n

See count.pl

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A test for association strength: Mutual Information

1988 AP corpus; N=44.3M

)()(

)(log

)()(

)(

log),(

2

2

YfXf

XYNfNYf

NXf

NXYf

YXMI

Data from (Church et al. 1991)

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Interpreting Mutual Information

High scores, e.g., strong supporter (8.85) indicates strongly associated in the corpus

MI is a logarithmic score. To convert it, recall that X=2 log2X

so, 28.85 461.44. So this is 461 X chance.

Low scores – powerful support (1.74): this is 3X chance, since 21.74 3

I fxy fx fy x y

1.74 2 1984 13,428 powerful support

I = log2 (2N/1984*13428) = 1.74

So, doesn’t necessarily mean weakly associated – could be due to data sparseness

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Mutual Information over Grammatical Relations

Parse a corpus

Determine subject-verb-object triples

Identify head nouns of subject and object NPs

Score subj-verb and verb-obj associations using MI

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Demo of Verb-Subj, Verb-Obj Parses

Who devours or what gets devoured?

Demo: http://www.cs.ualberta.ca/~lindek/demos/depindex.htm

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MI over verb-obj relations

Data from (Church et al. 1991)

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A Subj-Verb MI Example: Who does what in news?executive police politician

reprimand 16.36 shoot 17.37 clamor 16.94

conceal 17.46 raid 17.65 jockey 17.53

bank 18.27 arrest 17.96 wrangle 17.59

foresee 18.85 detain 18.04 woo 18.92

conspire 18.91 disperse 18.14 exploit 19.57

convene 19.69 interrogate 18.36 brand 19.65

plead 19.83 swoop 18.44 behave 19.72

sue 19.85 evict 18.46 dare 19.73

answer 20.02 bundle 18.50 sway 19.77

commit 20.04 manhandle 18.59 criticize 19.78

worry 20.04 search 18.60 flank 19.87

accompany 20.11 confiscate 18.63 proclaim 19.91

own 20.22 apprehend 18.71 annul 19.91

witness 20.28 round 18.78 favor 19.92

Data from (Schiffman et al. 2001)

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‘Famous’ Corpora

Must see: http://www.ldc.upenn.edu/Catalog/ Brown Corpus British National Corpus International Corpus of English Penn Treebank Lancaster-Oslo-Bergen Corpus Canadian Hansard Corpus U.N. Parallel Corpus TREC Corpora MUC Corpora English, Arabic, Chinese Gigawords Chinese, ArabicTreebanks North American News Text Corpus Multext East Corpus – ‘1984’ in multiple Eastern/Central

European langauges

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Links to Corpora

Corpora:

- Linguistic Data Consortium (LDC) http://www.ldc.upenn.edu/

- Oxford Text Archive http://sable.ox.ac.uk/ota/

- Project Gutenberg http://www.promo.net/pg/

- CORPORA list http://www.hd.uib.no/corpora/archive.html

Other:

- Chris Manning’s Corpora Page

- http://www-nlp.stanford.edu/links/statnlp.html#Corpora

- Michael Barlow’s Corpus Linguistics page http://www.ruf.rice.edu/~barlow/corpus.html

- Cathy Ball’s Corpora tutorial http://www.georgetown.edu/faculty/ballc/corpora/tutorial.html

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Summary: Introduction

Concordances and corpora are widely used and available, to help one to develop empirically-based linguistic theories and computer implementations

The linguistic items that can be counted are many, but “words” (defined appropriately) are basic items

The frequency distribution of words in any natural language is Zipfian

- Data sparseness is a basic problem when using observations in a corpus sample of language

Sequences of linguistic items (e.g., word sequences – n-grams) can also be counted, but the counts will be very rare for longer items

Associations between items can be easily computed

- e.g., associations between verbs and parser-discovered subjs or objs

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Outline

Topics

- Concordances

- Data sparseness


- Ngrams



- Annotation Issues




Case Studies

- metonymy



- TimeML

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Using POS in Concordances

Words Freq Freq/

Words

Fiction 2000

N

\bdeal_NN

1.5M 115 7.66

Fiction 2000

VB

1.5M 14 9.33

Gigaword

N

10.5M 2857 2.72

Gigaword

VB

10.5M 139 1.32

deal is more often a verbIn Fiction 2000

deal is more often a nounin English Gigaword

deal is more prevalent inFiction 2000 than Gigaword

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POS Tagging – What is it?

Given a sentence and a tagset of lexical categories, find the most likely tag for each word in the sentence

Tagset – e.g., Penn Treebank (45 tags, derived from the 87-tag Brown corpus tagset)

Note that many of the words may have unambiguous tags

Example

Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN

People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN

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More details of POS problem

How ambiguous?- Most words in English have only one Brown Corpus tag

Unambiguous (1 tag) 35,340 word types Ambiguous (2- 7 tags) 4,100 word types = 11.5%

- 7 tags: 1 word type “still”- But many of the most common words are ambiguous

Over 40% of Brown corpus tokens are ambiguous Obvious strategies may be suggested based on intuition

to/TO race/VB the/DT race/NN will/MD race/NN

Sentences can also contain unknown words for which tags have to be guessed: Secretariat/NNP is/VBZ

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Different English Part-of-Speech Tagsets

Brown corpus - 87 tags

- Allows compound tags

“I'm” tagged as PPSS+BEM

- PPSS for "non-3rd person nominative personal pronoun" and BEM for "am, 'm“

Others have derived their work from Brown Corpus

- LOB Corpus: 135 tags

- Lancaster UCREL Group: 165 tags

- London-Lund Corpus: 197 tags.

- BNC – 61 tags (C5)

- PTB – 45 tags

To see comparisons ad mappings of tagsets, go to www.comp.leeds.ac.uk/amalgam/tagsets/tagmenu.html

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PTB Tagset (36 main tags + 9 punctuation tags)

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PTB Tagset Development

Several changes were made to Brown Corpus tagset:- Recoverability

Lexical: Same treatment of Be, do, have, whereas BC gave each its own symbol

- Do/VB does/VBZ did/VBD doing/VBG done/VBN Syntactic: Since parse trees were used as part of

Treebank, conflated certain categories under the assumption that they would be recoverable from syntax

- subject vs. object pronouns (both PP)- subordinating conjunctions vs. prepositions on

being informed vs. on the table (both IN)- Preposition “to” vs. infinitive marker (both TO)

- Syntactic Function BC: the/DT one/CD vs. PTB: the/DT one/NN BC: both/ABX vs. PTB: both/PDT the boys, the boys both/RB, both/NNS

of the boys, both/CC boys and girls

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PTB Tagging Process

Tagset developed

Automatic tagging by rule-based and statistical pos taggers

Human correction using an editor embedded in Gnu Emacs

Takes under a month for humans to learn this (at 15 hours a week), and annotation speeds after a month exceed 3,000 words/hour

Inter-annotator disagreement (4 annotators, eight 2000-word docs) was 7.2% for the tagging task and 4.1% for the correcting task

Manual tagging took about 2X as long as correcting, with about 2X the inter-annotator disagreement rate and an error rate that was about 50% higher.

So, for certain problems, having a linguist correct automatically tagged output is far more efficient and leads to better reliability among linguists compared to having them annotate the text from scratch!

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Automatic POS tagging

http://complingone.georgetown.edu/~linguist/

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A Baseline Strategy

Choose the most likely tag for each ambiguous word, independent of previous words

- i.e., assign each token to the pos-category it occurred in most often in the training set

E.g., race – which pos is more likely in a corpus?

This strategy gives you 90% accuracy in controlled tests

- So, this “unigram baseline” must always be compared against

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Beyond the Baseline

Hand-coded rules

Sub-symbolic machine learning

Symbolic machine learning

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Machine Learning

Machines can learn from examples

Learning can be supervised or unsupervised

Given training data, machines analyze the data, and learn rules which generalize to new examples

Can be sub-symbolic (rule may be a mathematical function) –e.g. neural nets

Or it can be symbolic (rules are in a representation that is similar to representation used for hand-coded rules)

In general, machine learning approaches allow for more tuning to the needs of a corpus, and can be reused across corpora

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A Probabilistic Approach to POS tagging

What you want to do is find the “best sequence” of pos-tags C=C1..Cn for a sentence W=W1..Wn.

- (Here C1 is pos_tag(W1)). In other words, find a

sequence of pos tags Cthat maximizes P(C| W)

Using Bayes’ Rule, we can sayP(C| W) = P(W | C) * P(C) / P(W ) Since we are interested in

finding the value of C which maximizes the RHS, the denominator can be discarded, since it will be the same for every C

So, the problem is: Find C which maximizes

P(W | C) * P(C)

Example: He will race Possible sequences:

- He/PP will/MD race/NN- He/PP will/NN race/NN- He/PP will/MD race/VB- He/PP will/NN race/VB

W = W1 W2 W3 = He will race

C = C1 C2 C3- Choices:

C= PP MD NN C= PP NN NN C = PP MD VB C = PP NN VB

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Independence Assumptions

P(C1….Cn) i=1, n P(Ci| Ci-1)

- assumes that the event of a pos-tag occurring is independent of the event of any other pos-tag occurring, except for the immediately previous pos tag

From a linguistic standpoint, this seems an unreasonable assumption, due to long-distance dependencies

P(W1….Wn | C1….Cn) i=1, n P(Wi| Ci)

- assumes that the event of a word appearing in a category is independent of the event of any other word appearing in a category

Ditto

However, the proof of the pudding is in the eating!

- N-gram models work well for part-of-speech tagging

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But Data Sparseness Bites Again!

Lexical generation probabilities will lack observations for low-frequency and unknown words

Most systems do one of the following

- Smooth the counts

E.g., add a small number to unseen data (to zero counts). For example, assume a bigram not seen in the data has a very small probability, e.g., .0001.

Backoff bigrams with unigrams, etc.

- Use lots more data (you’ll still lose, thanks to Zipf!)

- Group items into classes, thus increasing class frequency

e.g., group words into ambiguity classes, based on their set of tags. For counting, alll words in an ambiguity class are treated as variants of the same ‘word’

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A Symbolic Learning Method

HMMs are subsymbolic – they don’t give you rules that you can inspect

A method called Transformational Rule Sequence learning (Brill algorithm) can be used for symbolic learning (among other approaches)

The rules (actually, a sequence of rules) are learnt from an annotated corpus

Performs at least as accurately as other statistical approaches

Has better treatment of context compared to HMMs

- rules which use the next (or previous) pos

HMMs just use P(Ci| Ci-1) or P(Ci| Ci-2Ci-1)

- rules which use the previous (next) word

HMMs just use P(Wi|Ci)

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Brill Algorithm (Overview)

Assume you are given a training corpus G (for gold standard)

First, create a tag-free version V of it

Notes:

- As the algorithm proceeds, each successive rule becomes narrower (covering fewer examples, i.e., changing fewer tags), but also potentially more accurate

- Some later rules may change tags changed by earlier rules

1. First label every word token in V with most likely tag for that word type from G. If this ‘initial state annotator’ is perfect, you’re done!

2. Then consider every possible transformational rule, selecting the one that leads to the most improvement in V using G to measure the error

3. Retag V based on this rule

4. Go back to 2, until there is no significant improvement in accuracy over previous iteration

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Brill Algorithm (Detailed)

1. Label every word token with its most likely tag (based on lexical generation probabilities).

2. List the positions of tagging errors and their counts, by comparing with ground-truth (GT)

3. For each error position, consider each instantiation I of X, Y, and Z in Rule template. If Y=GT, increment improvements[I], else increment errors[I].

4. Pick the I which results in the greatest error reduction, and add to outpute.g., VB NN PREV1OR2TAG DT

improves 98 errors, but produces 18 new errors, so net decrease of 80 errors

5. Apply that I to corpus6. Go to 2, unless stopping

criterion is reached

Most likely tag:

P(NN|race) = .98

P(VB|race) = .02

Is/VBZ expected/VBN to/TO race/NN tomorrow/NN

Rule template: Change a word from tag X to tag Y when previous tag is Z

Rule Instantiation to above example: NN VB PREV1OR2TAG TO

Applying this rule yields:

Is/VBZ expected/VBN to/TO race/VB tomorrow/NN

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Example of Error Reduction

From Eric Brill (1995):Computational Linguistics, 21, 4, p. 7

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Example of Learnt Rule Sequence

1. NN VB PREVTAG TO- to/TO race/NN->VB

2. VBP VB PREV1OR20R3TAG MD- might/MD vanish/VBP-> VB

3. NN VB PREV1OR2TAG MD- might/MD not/MD reply/NN -> VB

4. VB NN PREV1OR2TAG DT - the/DT great/JJ feast/VB->NN

5. VBD VBN PREV1OR20R3TAG VBZ- He/PP was/VBZ killed/VBD->VBN by/IN Chapman/NNP

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Handling Unknown Words

Can also use the Brill method

Guess NNP if capitalized, NN otherwise.

Or use the tag most common for words ending in the last 3 letters.

etc.

Example Learnt Rule Sequence for Unknown Words

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POS Tagging using Unsupervised Methods

Reason: Annotated data isn’t always available!

Example: the can Let’s take unambiguous

words from dictionary, and count their occurrences after the

- the .. elephant- the .. guardian

Conclusion: immediately after the, nouns are more common than verbs or modals

Initial state annotator: for each word, list all tags in dictionary

Transformation template: - Change tag of word to

tag Y if the previous (next) tag (word) is Z, where is a set of 2 or more tags

- Don’t change any other tags

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Error Reduction in Unsupervised Method

Let a rule to change to Y in context C be represented as Rule(, Y, C).

- Rule1: {VB, MD, NN} NN PREVWORD the- Rule2: {VB, MD, NN} VB PREVWORD the

Idea: - since annotated data isn’t available, score rules so as to

prefer those where Y appears much more frequently in the context C than all others in

frequency is measured by counting unambiguously tagged words

so, prefer {VB, MD, NN} NN PREVWORD the

to {VB, MD, NN} VB PREVWORD the

since dict-unambiguous nouns are more common in a corpus after the than dict-unambiguous verbs

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Summary: POS tagging

A variety of POS tagging schemes exist, even for a single language

Preparing a POS-tagged corpus requires, for efficiency, a combination of automatic tagging and human correction

Automatic part-of-speech tagging can use

- Hand-crafted rules based on inspecting a corpus

- Machine Learning-based approaches based on corpus statistics

e.g., HMM: lexical generation probability table, pos transition probability table

- Machine Learning-based approaches using rules derived automatically from a corpus

Combinations of different methods often improve performance

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Outline

Topics

- Concordances

- Data sparseness


- Ngrams



- Annotation Issues




Case Studies

- metonymy



- TimeML

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Adjective Ordering

*A political serious problem

*A social extravagant life

*red lovely hair

*old little lady

*green little men

Adjectives have been grouped into various classes to explain ordering phenomena

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Collins COBUILD L2 Grammar

qualitative < color < classifying

Qualitative – expresses a quality that someone or something has, e.g., sad, pretty, small, etc.

- Qualitative adjectives are gradable, i.e., the person or thing can have more or less of the quality

Classifying – used to identify the class something belongs to, i.e.., distinguishing

- financial help, American citizens.

- Classifying adjectives aren’t gradable.

So, the ordering reduces to

- Gradable < color < non-gradable A serious political problem

Lovely red hair

Big rectangular green Chinese carpet

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Vendler 68

A9 < A8 < …A2 < A1x <A1m < …<A1a

A9: probably, likely, certain

A8: useful, profitable, necessary

A7: possible, impossible

A6: clever, stupid, reasonable, nice, kind, thoughtful, considerate

A5: ready, willing, anxious

A4: easy

A3: slow, fast, good, bad, weak, careful, beautiful

A2: contrastive/polar adjectives: long-short, thick-thin, big-little, wide-narrow

A1j: verb-derivatives: washed

A1i: verb-derivatives: washing

A1h: luminous

A1g: rectangular

A1f: color adjectives

A1a: iron, steel, metal

big rectangular green Chinese carpet

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Other Adjective Ordering Theories

Goyvaerts 68 quality < size/length/shape < age < color < naturally < style < general < denominal

Quirck &

Greenbaum 73

Intensifying perfect < general-measurable careful wealthy < age young old < color < denominal material woollen scarf < denominal style Parisian dress

Dixon 82 value < dimension < physical property < speed < human propensity < age < color

Frawley 92 value < size < color (English, German, Hungarian, Polish,

Turkish, Hindi, Persian, Indonesian, Basque)

Collins COBUILD: gradable < color < non-gradable Goyvaerts, Q&G, Dixon: size < age < colorGoyvaerts, Q&G: color < denominalGoyvaerts, Dixon: shape < color

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Testing the Theories on Large Corpora

Selective coverage of a particular language or (small) set of languages

Based on categories that aren’t defined precisely that are computable

Based on small large numbers of examples

Test gradable < color < non-gradable

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Computable Tests for Gradable Adjectives

Submodifiers expressing gradation

- very|rather|somewhat|extremely A

But what about “very British”?

http://complingtwo.georgetown.edu/~gwilson/Tools/Adj/GW_Grad.txt

Periphrastic comparatives

- “more A than“ | "the most A“

Inflectional comparatives

- -er|-est

http://complingtwo.georgetown.edu/~gwilson/Tools/Adj/BothLists.txt

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Challenges: Data Sparseness

Data sparseness- Only some pairs will be present in a given

corpus few adjectives on the gradable list may be present

- Even fewer longer sequences will be present in a corpus

Use transitivity?- small < red, red < wooden --> small <

red < wooden?

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Challenges: Tool Incompleteness

Search pattern will return many non-examples- Collocations

common or marked ones - American “green card”- national Blue Cross

- Adjective Modification bright blue

- POS-tagging errors- May also miss many examples

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Results from Corpus Analysis

G < C < not G generally holds However, there are exceptions

- Classifying/Non-Gradable < ColorAfter all, the maple leaf replaced the British red

ensign as Canada's flag almost 30 years ago.http://complingtwo.georgetown.edu/~gwilson/Tools/Adj/Color2.html

where he stood on a stage festooned with balloons displaying the Palestinian green, white and red flag

http://complingtwo.georgetown.edu/~gwilson/Tools/Adj/Color4.html

- Color < Shapepaintings in which pink, roundish shapes, enriched

with flocking, gel, lentils and thread, suggest the insides of the female body.

http://complingtwo.georgetown.edu/~gwilson/Tools/Adj/Color4.html

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Summary: Adjective Ordering

It is possible to test concrete predictions of a linguistic theory in a corpus-based setting

The testing means that the machine searches for examples satisfying patterns that the human specifies

The patterns can pre-suppose a certain/high degree of automatic tagging, with attendant loss of accuracy

The patterns should be chosen so that they provide “handles” to identify the phenomena of interest

The patterns should be restricted enough that the number of examples the human has to judge is not infeasible

This is usually an iterative process

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Outline

Topics

- Concordances

- Data sparseness


- Ngrams



- Annotation Issues




Case Studies

- metonymy



- TimeML

66

The Art of Annotation 101

1. Define Goal

2. Eyeball Data (with the help of Computers)

3. Design Annotation Scheme

4. Develop Example-based Guidelines

5. Unless satisfied/exhausted, goto 1

6. WriteTraining Manuals

7. Initiate HumanTraining Sessions

8. Annotate Data / Train Computers

• Computers can also help with the annotation

9. Evaluate Humans and Computers

10. Unless satisfied/exhausted, goto 1

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Annottation Methodology Picture

RawCorpus

AnnotatedCorpus

InitialTagger

AnnotationEditor

AnnotationGuidelines

MachineLearningProgram

RawCorpus

LearnedRules

AnnotatedCorpus

RuleApply

KnowledgeBase?

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Goals of an Annotation Scheme

Simplicity – simple enough for a human to carry out

Precision – precise enough to be useful in CLI applications

Text-based – annotation of an item should be based on information conveyed by the text, rather than information conveyed by background information

Human-centered – should be based on what a human can infer from the text, rather than what a machine can currently do or not do

Reproducible – your annotation should be reproducible by other humans (i.e., inter-annotator agreement should be high)

- obviously, these other humans may have to have particular expertise and training

69

What Should An Annotation Contain

Additional Information about the text being annotated – e.g., EAGLES external and internal criteria

Information about the annotator – who, when, what version of tool, etc. (usually in meta-tags associated with the text)

The tagged text itself

Example:

http://www.emille.lancs.ac.uk/spoken.htm

70

External and Internal Criteria (EAGLES)

External: participants, occasion, social setting, communicative function

- origin: Aspects of the origin of the text that are thought to affect its structure or content.

- state: the appearance of the text, its layout and relation to non-textual matter, at the point when it is selected for the corpus.

- aims: the reason for making the text and the intended effect it is expected to have.

Internal: patterns of language use

- Topic (economics, sports, etc.)

- Style (formal/informal, etc.)

71

External Criteria – state (EAGLES)

Mode - spoken

participant awareness: surreptitious/warned/aware venue: studio/on location/telephone

- written Relation to the medium

- written: how it is laid out, the paper, print, etc.- spoken: the acoustic conditions, etc.

Relation to non-linguistic communicative matter - diagrams, illustrations, other media that are coupled with the

language in a communicative event. Appearance

- e.g., advertising leaflets, aspects of presentation that are unique in design and are important enough to have an effect on the language.

72

Examples of annotation schemes (changing the way we do business!)

POS tagging annotationPOS tagging annotation – Penn Treebank Scheme

Named entity annotation – ACE Scheme

Phrase Structure annotation – Penn Treebank scheme

Time Expression annotation – TIMEX2 Scheme

Protein Name Annotation – GU Scheme

Event Annotation – TimeML Scheme

Rhetorical Structure Annotation - RST Scheme

Coreference Annotation, Subjectivity Annotation, Gesture Annotation, Intonation Annotation, Metonymy Annotation, etc., etc.

Etc.

Several hundred schemes exist, for different problems in different languages

73

POS Tag Formats: Non-SGML – to SGML

CLAWS tagger: non-SGML

- What_DTQ can_VM0 CLAWS_NN2 do_VDI to_PRP Inderjeet_NP0 's_POS noonsense_NN1 text_NN1 ?_?

Brill tagger: non-SGML

- What/WP can/MD CLAWS/NNP do/VB to/TO Inderjeet/NNP 's/POS noonsense/NN text/NN ?/.

Alembic POS tagger:

- <s><lex pos=WP>What</lex> <lex pos=MD>can</lex> <lex pos=NNP>CLAWS</lex> <lex pos=VB>do</lex> <lex pos=TO>to</lex> <lex pos=NNP>Inderjeet</lex> <lex pos=POS>'</lex><lex pos=PRP>s</lex> <lex pos=VBP>noonsense</lex> <lex pos=NN>text</lex> <lex pos=".">?</lex></s>

Conversion to SGML is pretty trivial in such cases

74

SGML (Standard Generalized Markup Language)

A general markup language for text

- HTML is an instance of an SGML encoding

Text Encoding Initiative (TEI): defines SGML schemes for marking up humanities text resources as well as dictionaries

Examples:- <p><s>I’m really hungry

right now.</s><s>Oh, yeah?</s>

- <utt speak=“Fred” date=“10-Feb-1998”>That is an ugly couch.</utt>

Note: some elements (e.g., <p>) can consist just of a single tag

Character references: ways of referring to the non-ASCII characters using a numeric code

- å (this is in decimal) å (this is in hexadecimal)

å Entity references: are used to

encode a special character or sequence of characters via a symbolic name

- résum&eacute.;- &docdate;

75

DTDs

A document type definition, or DTD, is used to define a grammar of legal SGML structures for a document

- e.g., para should consist of one or more sentences and nothing else

SGML parser verifies that document is compliant with DTD

DTD’s can therefore be used for XML as well

DTDs can specify what attributes are required, in what order, what their legit values are, etc.

The DTDs are often ignored in practice!

DTD:

<!ENTITY writer SYSTEM "http://www.mysite.com/all-entities.dtd">

<!ATTLIST payment type (check|cash) "cash">

XML:

<author>&writer;</author>

<payment type="check">

76

XML

“Extensible Markup Language (XML) is a simple, very flexible text format derived from SGML.

Originally designed to meet the challenges of large-scale electronic publishing, XML is also playing an increasingly important role in the exchange of a wide variety of data on the Web and elsewhere.” www.w3.org/XML/

Defines a simplified subset of SGML, designed especially for Web applications

Unlike HTML, separates out display (e.g., XSL) from content (XML)

Example

<p/><s><lex pos=“WP”>What</lex> <lex pos=“MD”>can</lex></s>

Makes use of DTDs, but also RDF Schemas

77

RDF Schemas

Example of Real RDF Schema:

http://www.cs.brandeis.edu/~jamesp/arda/time/documentation/TimeML.xsd (see EVENT tag and attributes)

78

Inline versus Standoff Annotation

Usually, when tags are added, an annotation tool is used, to avoid spurious insertions or deletions

The annotation tool may use inline or standoff annotation

Inline – tags are stored internally in (a copy of) the source text.

- Tagged text can be substantially larger than original text

- Web pages are a good example – i.e., HTML tags

Standoff – tags are stored internally in separate files, with information as to what positions in the source text the tags occupy

- e.g., PERSON 335 337

- However, the annotation tool displays the text as if the tags were in-line

79

Summary: Annotation Issues

A ‘best-practices’ methodology is widely used for annotating corpora

The annotation process involves computational tools at all stages

Standard guidelines are available for use

To share annotated corpora (and to ensure their survivability), it is crucial that the data be represented in a standard rather than ad hoc format

XML provides a well-established, Web-compliant standard for markup languages

DTDs and RDF provide mechanisms for checking well-formedness of annotation

80

Outline

Topics

- Concordances

- Data sparseness


- Ngrams



- Annotation Issues




Case Studies

- metonymy



- TimeML

81

Background

Deborah Schiffrin. Anaphoric then: aspectual, textual, and epistemic meaning. Linguistics 30 (1992), 753-792

Schiffrin xamines uses of then in data elicited via 20 sociolinguistic interviews, each an hour long

Distinguishes two anaphoric temporal senses, showing that they are differentiated by clause position

Shows that they have systematic effects on aspectual interpretation

A parallel argument is made for two epistemic temporal senses

82

Schiffrin: Temporal and Non-Temporal Senses

Anaphoric Senses- ‘Narrative’ temporal sense (shifts reference time)

And then I uh lived there until I was sixteen- Continuing Temporal sense (continues a previous

reference time) I was only a little boy then.

Epistemic senses- Conditional ‘sentences’ (rare, but often have temporal

antecedents in her data) But if I think about it for a few days -- well, then I

seem to remember a great deal …if I’m still in the situation where I am now….I’m, not

gonna have no more then- Initiation-response-evaluation sequences (‘in that

case’?) Freda: Do y’ still need the light? Debby: Um. Freda” W’ll have t’ go in then. Because the bugs are

out.

83

Schiffrin’s Argument (Simplified) and Its Test

Shifting RT thens (call these Narrative) & then in if-then conditionals

- similar semantic function

- mainly clause-initial

Continuing RT thens (call these Temporal) & IRE thens

- similar semantic function

- mainly clause final

- stative verb more likely (since RT overlaps, verbs conveying duration are expected)

Call the rest Other

- isn’t differentiated into if-then versus IRE

- So, only part of her claims tested

84

So, What do we do Then?

Define environments of interest, each one defined by a pattern

For each environment

1. Find examples matching the pattern

2. If classifying the examples is manageable, carry it out and stop

3. Otherwise restrict the environment by adding new elements to the pattern, and go back to 1

So, for each final environment, we claim that X% of the examples in that environment are of a particular class

Initial ‘then’ Pattern: (^|_CC|_RB)\s*then\w+\s+\w

Final ‘then’ Pattern: [^\,]\s+then[\.\?\'\;\!\:]

85

Exceptions

Non-Narrative Initial ‘then’

then there [be]

then come

then again

then and now

only then

even then

so then

Non-Temporal Final ‘then’

What then?

All right/OK [,] then

And then?

86

Results

Written Fiction 2000 Spoken Broadcast News Written Gigaword News

T N O T N O T N O

Clause Initial 1.73

(23/13

22)

96.67

(1276/

1322)

1.58

(21/13

22)

.73

(6/81

8)

93.88(

768/81

8)

5.3

(44/81

8)

3.64

(27/740

)

75.94

(562/74

0)

20.40

(151/74

0)

Clause Final 71.81

(79/11

0)

2.72

(3/110)

25.45

(28/11

0)

72.61

(61/8

4)

5.95

(5/84)

21.42

(18/84)

93.23

(179/19

2)

0 6.77

(13/192

)

Other is a presence in final position in fiction and broadcast news, and in initial position in print news. Is this real or artifact of catch-all class?

Conclusion: only part of her claims tested. But those claims are borne out across three different genres and much more data!

87

Outline

Topics

- Concordances

- Data sparseness


- Ngrams



- Annotation Issues




Case Studies

- metonymy



- TimeML

88

Considerations in Inter-Annotator Agreement

Size of tagset

Structure of tagset

Clarity of Guidelines

Number of raters

Experience of raters

Training of raters

- Independent ratings (preferred)

- Consensus (not preferred)

Exact, partial, and equivalent matches

Metrics

Lessons Learned: Disagreement patterns suggest guideline revisions

89

Protein Names

Considerable variability in the forms of the names

Multiple naming conventions

Researchers may name a newly discovered protein based on

- function

- sequence features

- gene name

- cellular location

- molecular weight

- discoverer

- or other properties

Prolific use of abbreviations and acronyms

fushi tarazu 1 factor homolog

Fushi tarazu factor (Drosophila) homolog 1

FTZ-F1 homolog ELP

steroid/thyroid/retinoic nuclear hormone receptor homolog nhr-35

V-INT 2 murine mammary tumor virus integration site oncogene homolog

fibroblast growth factor 1 (acidic) isoform 1 precursor

nuclear hormone receptor subfamily 5, Group A, member 1

90

Guidelines v1 TOC

91

Agreement Metrics

Reference Candidate

Yes No

Yes TP FN

No FP TN

Measure Definition

Percentage Agreement

100*(TP+TN)/(TP+FP+TN+FN)

Precision TP/(TP+FP)

Recall TP/(TP+FN)

(Balanced) F-Measure

2*Precision*Recall/(Precision+Recall)

92

Example for F-measure: Scorer Output (Protein Name Tagging)

REFERENCE CANDIDATE

CORR FTZ-F1 homolog ELP | FTZ-F1 homolog ELP INCO M2-LHX3 | M2SPUR | -SPUR | LHX3

Precision = ¼ = 0.25

Recall = ½ = 0.5

F-measure = 2 * ¼ * ½ / ( ¼ + ½ ) = 0.33

93

The importance of disagreement

Measuring inter-annotator agreement is very useful in “debugging” the annotation scheme

Disagreement can lead to improvements in the annotation scheme

Extreme disagreement can lead to abandonment of the scheme

94

V2 Assessment (ABS2)

Old Guidelines

- protein 0.71 F

- acronym 0.85 F

- array-protein 0.15

F

New Guidelines

- protein 0.86 F

- long-form 0.71 F

these are only

~4% of tags

Coders

Correct

Precision

Recall

F-mea-

sure

<protein>

A1-A3 4497 0.874

0.852

0.863

A1-A4 4769 0.884

0.904

0.894

A3-A4 4476 0.830

0.870

0.849

Average

0.862 0.875 0.868

<long-form>

A1-A3 172 0.720

0.599

0.654

A1-A4 241 0.837

0.840

0.838

A3-A4 175 0.608

0.732

0.664

Average

0.721 0.723 0.718

95

TIMEX2 Annotation Scheme

Time Points <TIMEX2 VAL="2000-W42">the third week of October</TIMEX2>

Durations <TIMEX2 VAL=“PT30M”>half an hour long</TIMEX2>

Indexicality <TIMEX2 VAL=“2000-10-04”>tomorrow</TIMEX2>

Sets <TIMEX2 VAL=”XXXX-WXX-2" SET="YES” PERIODICITY="F1W" GRANULARITY=“G1D”>every Tuesday</TIMEX2>

Fuzziness <TIMEX2 VAL=“1990-SU”>Summer of 1990 </TIMEX2>

<TIMEX2 VAL=“1999-07-15TMO”>This morning</TIMEX2>

Non-specificity <TIMEX2 VAL="XXXX-04" NON_SPECIFIC=”YES”>April</TIMEX2> is usually wet.

For guidelines, tools, and corpora, please see timex2.mitre.org

96

TIMEX2 Inter-Annotator Agreement

tag pos act corr prec rec f f-TempEx

extent 6421 6369 5064 0.795101 0.788662 0.791882 0.762098val 5324 5296 4578 0.864426 0.85988 0.862153 0.829071granularity 289 312 165 0.535168 0.486111 0.51064 0.440212mod 494 451 363 0.813776 0.723356 0.768566 0.778878non_specific 143 92 28 0.304348 0.195804 0.250076 0periodicity 244 253 208 0.822134 0.852459 0.837297 0.811268set 300 316 232 0.734177 0.773333 0.753755 0.81465

193 NYT news docs5 annotators10 pairs of annotators

Human annotation quality is ‘acceptable’ on EXTENT and VAL Poor performance on Granularity and Non-Specific

But only a small number of instances of these (200 ~ 6000)

Annotators deviate from guidelines, and produce systematic errors (fatigue?) several years ago: PXY instead of PAST_REF

all day: P1D instead of YYYY-MM-DD

97

TempEx in Qanda

98

Summary: Inter-Annotator Reliability

There’s no point going on with an annotation scheme if it can’t be reproduced

There are standard methods for measuring inter-annotator reliability

An analysis of inter-annotator disagreements is critical for “debugging” an annotation scheme

99

Outline

Topics

- Concordances

- Data sparseness


- Ngrams



- Annotation Issues




Case Studies

- metonymy



- TimeML

100

Information Extraction

Types

- Flag names of people, organizations, places,…

- Flag and normalize time expressions, phrases such as time expressions, measure phrases, currency expressions, etc.

- Group coreferring expressions together

- Find relations between named entities (works for, located at, etc.)

- Find events mentioned in the text

- Find relations between events and entities

- A hot commercial technology!

Example patterns:

- Mr. ---,

- , Ill.

101

Message Understanding Conferences (MUCs)

Idea: precise tasks to measure success, rather than test suite of input and logical forms.

MUC-1 1987 and MUC-2 1989 - messages about navy operations

MUC-3 1991 and MIC-4 1992 - news articles and transcripts of radio broadcasts about terrorist activity

MUC-5 1993 - news articles about joint ventures and microelectronics

MUC-6 1995 - news articles about management changes, + additional tasks of named entity recognition, coreference, and template element

MUC-7 1998 – mostly multilingual information extraction

Has also been applied to hundreds of other domains - scientific articles, etc., etc.

102

Historical Perspective

Until MUC-3 (1993), many IE systems used a Knowledge Engineering

approach

- They did something like full chart parsing with a unification-based

grammar with full logical forms, a rich lexicon and KB

- E.g., SRI’s Tacitus

Then, they discovered that things could work much faster using finite-state

methods and partial parsing

And that using domain-specific rather than general purpose lexicons

simplified parsing (less ambiguity due to fewer irrelevant senses)

And that these methods worked even better for the IE tasks

- E.g., SRI’s Fastus, SRA’s Nametag

Meanwhile, people also started using statistical learning methods from

annotated corpora

- Including CFG parsing

103

An instantiated scenario template

Wall Street Journal, 06/15/88

MAXICARE HEALTH PLANS INC and UNIVERSAL HEALTH SERVICES INC have dissolved a joint venture which provided health services.

<TEMPLATE-8806150049-1> := DOC NR: 8806150049 CONTENT: <TIE_UP_RELATIONSHIP-8806150049-1> DATE TEMPLATE COMPLETED: 311292 EXTRACTION TIME: 0

Source

104

Templates Can get Complex! (MUC-5)

<TEMPLATE-8806150049-1> := DOC NR: 8806150049 CONTENT: <TIE_UP_RELATIONSHIP-8806150049-1> DATE TEMPLATE COMPLETED: 311292 EXTRACTION TIME: 0<TIE_UP_RELATIONSHIP-8806150049-1> := TIE-UP STATUS: DISSOLVED ENTITY: <ENTITY-8806150049-1> <ENTITY-8806150049-2> JOINT VENTURE CO: <ENTITY-8806150049-3> OWNERSHIP: <OWNERSHIP-8806150049-1> <OWNERSHIP-8806150049-2> ACTIVITY: <ACTIVITY-8806150049-1><ENTITY-8806150049-1> := NAME: Maxicare Health Plans INC ALIASES: "Maxicare" LOCATION: Los Angeles (CITY 4) California (PROVINCE 1) United States (COUNTRY) TYPE: COMPANY ENTITY RELATIONSHIP: <ENTITY_RELATIONSHIP-8806150049-1><ENTITY-8806150049-2> := NAME: Universal Health Services INC ALIASES: "Universal Health" LOCATION: King of Prussia (CITY) Pennsylvania (PROVINCE 1) United States (COUNTRY) TYPE: COMPANY ENTITY RELATIONSHIP: <ENTITY_RELATIONSHIP-8806150049-1><ACTIVITY-8806150049-1> := INDUSTRY: <INDUSTRY-8806150049-1> ACTIVITY-SITE: (<FACILITY-8806150049-1> <ENTITY-8806150049-3>)<INDUSTRY-8806150049-1> := INDUSTRY-TYPE: SERVICE PRODUCT/SERVICE: (80 "a joint venture Nevada health maintenance [organization]")

105

2002 Automatic Content Extraction (ACE) Program: Entity Types

Person

Organization

(Place)

- Location – e.g., geographical areas, landmasses, bodies of water, geological formations

- Geo-Political Entity – e.g., nations, states, cities

Created due to metonymies involving this class of places

The riots in Miami

Miami imposed a curfew

Miami railed against a curfew

Facility – buildings, streets, airports, etc.

106

ACE Entity Attributes and Relations

Attributes

- Name: An entity mentioned by name

- Pronoun

- Nominal

Relations

- AT: based-in, located, residence

- NEAR: relative-location

- PART: part-of, subsidiary, other

- ROLE: affiliate-partner, citizen-of, client, founder, general-staff, manager, member, owner, other

- SOCIAL: associate, grandparent, parent, sibling, spouse, other-relative, other-personal, other-professional

107

Designing an Information Extraction Task

Define the overall task

Collect a corpus

Design an Annotation Scheme

- linguistic theories help

Use Annotation Tools

- - authoring tools

- -automatic extraction tools

Apply to annotation to corpus, assessing reliability

Use training portion of corpus to train information extraction (IE) systems

Use test portion to test IE systems, using a scoring program

108

Annotation Tools

Specialized authoring tools used for marking up text without damaging it

Some tools are tied to particular annotation schemes

109

Annotation Tool Example: Alembic Workbench

110

Callisto (Java successor to Alembic Workbench)

Relationship Annotation: Callisto

112

Steps in Information Extraction

Tokenization- Language Identification- Document Zoning- Sentence and Word Tokenization

Morphological and Lexical Processing - Tagging entities of interest- Specific trigger lexicons- Dealing with unknown words- Part-of-Speech Tagging- Word-Sense Tagging- Morphological Analysis

Parsing- Finite-State Parsing (usually just chunking)

Domain Semantics- Coreference- Merging Partial Results

113

Morphological Analysis

Inflectional morphology, mostly

For simple languages (English, Japanese) – simple inflectional module suffices

For more complex languages (Spanish) – a finite-state transducer is used

For morphologically very complex languages (Arabic, Hebrew) – complex finite state transducer architectures

For languages with productive noun compounding (German) – specialized module needed

114

Finite-State Parsing for IE

A.C. Nielesen CO. NG said VG George Garrick NG, 40 years old, president NG of

information Resources Inc. NG's London-based European

Information Services operationNG, will becomeVG presidentNG and chief operating officerNG of Nielsen Marketing Research USANG, a unit NG of Dun & Bradstree Corp. NG

First find NG, VG, particles; ignore PP attachment; ignore clause boundaries; maybe ignore modifiers that aren’t domain-relevant

Later transducers handle more complex phenomena:

- relative clauses (e.g., look for second verb for marking end of rc; subject relatives: associate subject with first and second verb; object relatives: associate object with head noun before rel mod)

- general clause segmentation

- coordination

- appositives

- PP argument attachment (only for verbs important in domain whose subcat info is provided – rest are adverbial adjuncts)

115

Example Text Processing

KEY:

Trigger word tagging

Named Entity tagging

Chunk parsing: NGs, VGs, preps, conjunctions

Bridgestone Sports Co. said Friday it has set up a joint venture in Taiwan with a local concern and a Japanese trading house to produce golf clubs to be shipped to Japan.

CompanyNG Set-UPVG Joint-VentureNG with CompanyNG

ProduceVG ProductNG

The joint venture, Bridgestone Sports Taiwan Cp., capitalized at 20 million new Taiwan dollars, will start production in January 1990 with production of 20,000 iron and “metal wood” clubs a month.

116

Merging Structures

Activity:

Type: PRODUCTION

Company:

Product: golf clubs

Start-date:

Bridgestone Sports Co. said Friday it has set up a joint venture in Taiwan with a local concern and a Japanese trading house to produce golf clubs to be shipped to Japan.

Activity:

Type: PRODUCTION

Company: Bridgestone Sports Taiwan Co

Product: iron and “metal wood” clubs

Start-date: DURING 1990

The joint venture, Bridgestone Sports Taiwan Cp., capitalized at 20 million new Taiwan dollars, will start production in January 1990 with production of 20,000 iron and “metal wood” clubs a month.

117

Coreference

Coreference means establishing referential relations between expressions.

- Pronouns ..Mr. Gates …he, the testimony….it- Definite NPs Microsoft….the company- Indefinite NPs the building…an apartment- Proper Names Bill Gates…William Gates…. Mr. Gates- Temporal Expressions today, three weeks from Monday- Headless Determiners all, the one, five- Prenominals aluminum siding …the price of aluminum- Events they attacked at dawn…the attack

Types of relationships:- Identity, Part-whole- Set-subset the jurors…five ….- Set-member the jurors…on

118

Statistical Named Entity Tagging

Typically, treat it as a word-level tagging problem

- To get phrase-level tags, one could greedily concatenate adjacent tags

this will fail to separate ‘like’ tags

Approaches can separately model words at start, end, or middle of name

- BBN Identifinder does that

P(C|W) = P(W, C)/P(W)

argmaxCP(W, C)

P(Ci|Ci-1, wi-1) first word in a name

* P(<w, f>i=first|Ci, Ci-1) first word in a name

* P(<w,f>i|<w,f>i-1, Ci) all but the first word in a name

Word features f includes information about capitalization, initials, etc.

119

Information Extraction Metrics

Precision: Correct Answers/Answers Produced

Recall: Correct Answers /Total Possible Correct

F-measure - uses a parameter to weight precision versus recall (=1 for balance)

F = (B2+1) PR / B2(P+R)

F =.6 for the relationship/event extraction task (ceiling) in MUC

F = .95+ for named entity task in MUC

= .8 or so for coreference task

120

40

50

60

70

80

90

100

% C

OR

RE

CT

IE and QA Evaluations

Current status for various information extraction and question-answering components

Current status for various information extraction and question-answering components

Event extractionEvent extraction

Names in English

Names in EnglishNames in

Japanese Names in Chinese

Names in Japanese Names

in Chinese

Question AnsweringQuestion Answering

RelationsRelations

Names from audio @ 0%

15% word error

Names from audio @ 0%

15% word error

121

Summary: Information Extraction

A variety of IE tasks and methods are available

Named entities, relations, and event templates can be filled, as well as coreference relations

Linguistic information used can be hand-crafted or corpus-based

Domain knowledge, where needed, is hand-crafted

Performance on names is better than on relations, while “deep” templates have shown a 60% ceiling effect

122

Outline

Topics

- Concordances

- Data sparseness


- Ngrams



- Annotation Issues




Case Studies

- metonymy



- TimeML

123

Motivation for Temporal Information Extraction

Story Understanding

- Question-answering

- Summarization

Focus on temporal aspects of narrative

124

Chronology of ‘The Marathon’ (mini-story)

Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her.

02172004 02182004

run

twist ankle

du

rin

gfi

nis

hes

or

du

rin

g

before

push befo

re

du

rin

g

1. When did the running occur?Yesterday.2. When did the twisting occur?Yesterday, during the running.3. Did the pushing occur before the twisting?Yes.4. Did Holly keep running after twisting her ankle?5. Maybe not????

125

Factors influencing Event Ordering(1) Max entered the room. He had drunk a lot of wine.

TENSE: Past perfect indicates drinking precedes entering.

(2) Max entered the room. Mary was seated behind the desk.

ASPECT: State of ‘being seated’ overlaps with ‘entering’.

(3) He had borrowed some shirts from local villagers after his backpack went down.

TEMPORAL MODIFIER: Going down precedes borrowing, based on temporal adverbial after

(4) Iraq was defeated during the Gulf War. In ancient times it was the cradle of civilization.

TIMEX: Being the cradle precedes being defeated, based on explicit time expression.

(5) Max stood up. John greeted him.

NARR_CONVENTION: Narrative convention applies, with ‘standing up’ preceding ‘greeting’

(6) Max fell. John pushed him.

DISCOURSE_REL: Narrative convention overridden, based on Explanation relation

(7) A drunken man died in the central Philippines when he put a firecracker under his armpit.

DISCOURSE_REL: dying after putting, with temporal modifier used to instantiate Explanation relation

(8) U.N. Secretary- General Boutros Boutros-Ghali Sunday opened a meeting of .... Boutros-Ghali arrived in Nairobi from South Africa, accompanied by Michel...

WORLD KNOWLEDGE: arrival at the place of a meeting precedes opening a meeting

126

What’s Needed for Computing Chronologies?

Representation of tense and aspect

Representation of events and time

Linking of events and time

Result: a temporal constraint network

- Here, both events and times are represented as pairs of points (nodes)

- Ordering relations (edges) are <, =

Chronology

Time

Event Event-participants

-participants

02172004

run

du

rin

g

02172004

run

<x1 x2

y1 y2

>

Yesterday, Holly was running ….

<

<

[Verhagen 2004]

127

TimeML Annotation

TimeML is a proposed metadata standard for markup of events and their temporal anchoring and ordering

Consists of EVENT tags, TIMEX3 tags, and LINK tags

- EVENTS are grouped into classes and have tense and aspect features

- LINKS include overt and covert links

Can be within or across sentences

128

How TimeML Differs from Previous Markups

Extends TIMEX2 annotation to TIMEX3- Temporal Functions: three years ago- Anchors to events and other temporal expressions: three

years after the Gulf War- Addresses problem with Granularity/Periodicity: three days

every month- Inserts start/end points for Durations: two weeks from June

7 Identifies signals determining interpretation of temporal expressions;

- Temporal Prepositions: for, during, on, at;- Temporal Connectives: before, after, while.

Identifies event expressions; - tensed verbs; has left, was captured, will resign;- stative adjectives; sunken, stalled, on board;- event nominals; merger, Military Operation, Gulf War;

Creates dependencies between events and times:- Anchoring; John left on Monday.- Orderings; The party happened after graduation.- Embedding; John said Mary left.

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TLINKTLINK or Temporal Link represents the temporal relationship

holding between events or between an event and a time, and establishes a link between the involved entities, making explicit if they are:

• Simultaneous (happening at the same time)• Identical: (referring to the same event)• John drove to Boston. During his drive he ate a

donut. • One before the other:

• The police looked into the slayings of 14 women.In six of the cases suspects have already been arrested.

• One immediately before the other: • All passengers died when the plane crashed

into the mountain. • One including the other: • John arrived in Boston last Thursday.• One holding during the duration of the other: • One being the beginning of the other: • John was in the gym between 6:00 p.m. and

7:00 p.m.• One being the ending of the other: • John was in the gym between 6:00 p.m. and

7:00 p.m.

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SLINKSLINK or Subordination Link is used for contexts introducing relations between two events, or an event and a signal, of the following sort: Modal: Relation introduced mostly by modal verbs (should, could, would, etc.) and events that introduce a reference to a possible world --mainly I_STATEs:

John should have bought some wine. Mary wanted John to buy some wine.

Factive: Certain verbs introduce an entailment (or presupposition) of the argument's veracity. They include forget in the tensed complement, regret, manage:

John forgot that he was in Boston last year. Mary regrets that she didn't marry John.

Counterfactive: The event introduces a presupposition about the non-veracity of its argument: forget (to), unable to (in past tense), prevent, cancel, avoid, decline, etc.

John forgot to buy some wine. John prevented the divorce.

Evidential: Evidential relations are introduced by REPORTING or PERCEPTION: John said he bought some wine. Mary saw John carrying only beer.

Negative evidential: Introduced by REPORTING (and PERCEPTION?) events conveying negative polarity:

John denied he bought only beer. Negative: Introduced only by negative particles (not, nor, neither, etc.), which will be marked as SIGNALs, with respect to the events they are modifying:

John didn't forget to buy some wine. John did not want to marry Mary.

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Role of the machine in human annotation

In cases of dense annotation (events, pos tags, word-sense tags, etc.), it can be too tedious for a human to annotate everything

In such cases, it’s helpful to have a computer program pre-annotate the data that the human then corrects

The machine can also interact to flag invalid entries

The machine can also provide visualization

The machine can also augment the annotation with information that can be inferred

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Annotating Chronology in The Marathon

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Pre-Closure

134

Post-Closure

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Automatic TIMEX2 tagging


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TimeML Annotation Issues

Problems Weaknesses in guidelines

- Links between subordinate clause and main clause of same/diff sentence

- Difficulties in annotating states

Granularity of temporal relations (72% agreement on temporal relations on common links)

Density of links. Number of links is quadratic in the number of events, but less than half the eventualities are linked.

So, inter-annotator agreement on links likely to be low.

Solutions Adding more annotation

conventions Lightening the annotation. Expanding annotation

using temporal reasoning. Using heavily mixed-

initiative approach Providing user with

visualization tools during annotation.

Note: such problems are characteristic of semantic and discourse-level annotations!

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TimeBank Browser and TimeML tools

http://corpora.dutchboy.net/timebank/


Strategy for Automatically Inferring Linguistic Information

Develop a corpus of TimeML annotated documents

- TimeML represents temporal adverbials, tense, grammatical aspect, temporal relations

- Takes into account subordination and (to an extent) vagueness

- Work on metric constraints for durations of states is ongoing (Hobbs)

Develop initial computer taggers to tag Events, Times, and Links in the corpus

Correct the corpus using a human

Ensure that the annotations can be reproduced accurately

- Inter-annotator reliability

Use the corpus to train improved computer taggers

At the Florist’s (mini-story)

• a. John went into the florist shop.

• b. He had promised Mary some flowers.

• c. She said she wouldn’t forgive him if he forgot.

• d. So he picked out three red roses.

• From (Webber 1988)

Chronology of At the Florist’s

At the Florist’s: A Rhetorical Structure Theory account

• Assumes abstract nodes which are Rhetorical Relations

• Rhetorical relation annotations are not easily reproduced– question of inter-

annotator reliability

Narration

Explanation Ed

Ea Elaboration

Narration

Eb Ec

Temporal Relations as Surrogates for Rhetorical Relations

• When E1 is left-sibling of E2 and E1 < E2, then typically, Narration(E1, E2)

• When E1 is right-sibling of E2 and E1 < E2, then typically Explanation(E2, E1)

• When E2 is a child node of E1, then typically Elaboration(E1, E2)

constraints: {Eb < Ec, Ec < Ea, Ea < Ed}

a. John went into the florist shop. b. He had promised Mary some flowers. c. She said she wouldn’t forgive him if he forgot. d. So he picked out three red roses.

Narr

Elab

Expl

Temporal Discourse Model Annotation Conventions

1. Each tree is rooted in an abstract node. 2. In the absence of any temporal adverbials or discourse

markers, a tense shift will license the creation of an abstract node, with the tense shifted event being the leftmost daughter of the abstract node. The abstract node will then be inserted as the child of the immediately preceding text node.

3. In the absence of temporal adverbials and discourse markers, a stative event will always be placed as a child of the immediately preceding text event when the latter is non-stative, and as a sibling of the previous event when the latter is stative (as in a scene-setting fragment of discourse).

Representing States

• Approach: Minimality– A tensed stative predicate

is represented as a node in the tree (progressives are treated as stative).

John walked home. He was feeling great.

– We represent the state of feeling great as being minimally a part of the event of walking, without committing to whether it extends before or after the event

– A constraint is added to C indicating that this inclusion is minimal.

• Problem: IncompletenessMax entered the room. He

was wearing a black shirt The system will not know

whether the shirt was worn after he entered the room.

TDMs and DRT

EaEbEcxyzt1t2t3 [enter(Ea, x, theWhiteHart) & man (x) & PROG(wear(Eb, x, y) & black-jacket(y) &serve(Ec, Bill, x, z) & beer(z) & t1 < n & Ea t1 & t2 < n & Eb o t2 & Eb Ea & t3 < n & Ec t3 & Ea < Ec]

What’s Needed for Computing TDMs?

• A Corpus of TDMs, annotated with high inter-annotator reliability

• ‘Syntactic’ parsers for TDMs, trained on the corpus

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Conclusion

There are lots of computational tools for manual and automatic annotation of linguistic data and exploration of linguistic hypotheses

The automatic tools aren’t perfect, but neither are humans!

An annotation scheme must be tested using guidelines and inter-annotator reliability

Annotations must be prepared and used within standard XML-based frameworks

There are many costs and tradeoffs in corpus preparation

The yields can considerably speed up the pace of linguistic research

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Desiderata for Indian Language Work

The data needs to be encoded using standard character encoding schemes – UNICODE, or else ISCII

Annotation needs to follow the best-practices methodology, including proof of replicability, and XML representation

Experience has shown that linguists and computer scientists can work in synergy on this

Once corpora are prepared according to these guidelines, automatic tools can be developed in India and abroad and used to improve linguistic processing of Indian languages

- Morphological analyzers, stemmers, etc.

- Part-of-speech taggers

- Syntactic Parsers

- Word-Sense Disambiguators

- Temporal Taggers

- Information Extraction Systems

- Text Summarizers

- Statistical MT Systems

- etc.

Free Resources (contact me)

• TIMEX2 corpora and tools: timex2.mitre.org (English, Korean, Spanish)

• TimeML and annotation tools: www.timeml.org• AQUAINT corpus, and TimeML software: watch this

space• PRONTO and iprolink corpora, guidelines, tagsets• (see my web site)

The Changing Environment

• If statistical rules induced from examples perform just as well as rules derived from intuition, then this suggests that probabilistic statistical linguistic rules might help explain or model human linguistic behavior.

• It also suggests that humans might learn from experience by means of induction using statistical regularities.

• For many years, corpus linguistic research rarely examined statistics above the level of words, due to the lack of availability of broad-coverage parsers and statistical models that could handle syntax and other levels of ‘hidden structure’ (Manning 2003).

• The present climate with plenty of tools and statistical models, should allow corpus linguistics to extend its descriptive and explanatory scope dramatically.

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Ngrams Details

Consider a sequence of words W1…Wn, “I saw a rabbit”. What’s P(W1…Wn)? Note that we can’t find sequences of

length n, and count them - there won’t be enough data. Chain Rule of probability:

P(W1, .. ,Wn ) = P(W1)P(W2|W1) P(W3|W1,W2)..P(Wn|W1,W2, ..,Wn-1 )

- But you still have the problem of lacking enough data Bigram model

- Approximates P(Wn|W1…Wn-1) by P(Wn|Wn-1)- Assumes the probability of a word depends just on the

previous word. This means, that you don’t have to look back more than one word.

- P(I saw a rabbit) = P(I|<s>)*P(saw|I)*P(a|saw)*P(rabbit|a)- More generally: P(W1….Wn) i=1, n P(Wi| Wi-1)

A trigram model, would look 2 words back into the past- P(I saw a rabbit) =P(saw|<s> I)*P(a| I saw)*P(rabbit|saw a)

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POS Tagging Based on N-grams

Problem: Find C which maximizes P(W | C) * P(C)

Here W=W1..Wn and C=C1..Cn (these were sequences, remember?)

P(W1, .. ,Wn ) = P(W1)P(W2|W1) P(W3|W1,W2)..P(Wn|W1,W2, ..,Wn-1 )

- Using the bigram model, we get:

P(W1….Wn | C1….Cn) i=1, n P(Wi| Ci)

P(C1….Cn) i=1, n P(Ci| Ci-1)

So, we want to find the value of C1..Cn which maximizes:

i=1, n P(Wi| Ci) * P(Ci| Ci-1)

lexical generationprobabilities, estimatedfrom training data

posbigramprobs, estimatedfrom training data

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Problems in Event Anchoring

States

- John walked home. He was feeling great.

How long does “feeling great” last?

- => We need a “minimal” duration for states- a. Mary entered the President’s Office.b. A copy of the budget was on the

president’s desk. c. The president’s financial advisor stood beside it. d. The president sat regarding both admiringly. e. The advisor spoke. (Dowty 1986)

Was the budget on the desk before she entered the office?

- => “perceived scene” presents an imperfective view of states, not indicating their true onsets

Vagueness

The attack lasted 2-3 weeks.

Recently, Holly turned 16.

Next summer, Holly may run

Three days later, David pushed her

- => temporal reasoning has to deal with vagueness

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Problems in Event Anchoring (contd)

Vagueness (contd)

- John hurried to Mary’s house after work. But Mary had already left for dinner.

- => we need to track ‘reference time’ and decide when reference times coincide

Modality

- John should have brought some wine.

Did he bring wine? No.

- John prevented the divorce.

Did the divorce happen? No.

=> we need to know about subordination Implicit Information

Yesterday, Holly fell. (implicit “on”)

Holly fell. David pushed her. (implicit “because”)

=> we need discourse modeling

1 computational tools for linguists inderjeet mani georgetown university [email protected]

Documents

data slide

corpus preparation slide

applied linguistics

timeml slide

messy data

different corpus

chomskys critique of

metonymy metonymy