cse6339 3.0 introduction to computational linguistics tuesdays, thursdays 14:30-16:00 – south ross...
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CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 1
Final HPSGs
Cleaning up and final aspects, semantics, overview to statistical NLP
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 2
HPSGs
An Overlooked Topic: Complements vs. Modifiers• Intuitive idea: Complements introduce essential
participants in the situation denoted; modifiers refine the description.
• Generally accepted distinction, but disputes over individual cases.
• Linguists rely on heuristics to decide how to analyze questionable cases (usually PPs).
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 3
HPSGs
Heuristics for Complements vs. Modifiers
• Obligatory PPs are usually complements.
• Temporal & locative PPs are usually modifiers.
• An entailment test: If X Ved (NP) PP does not entail X did something PP, then the PP is a complement.
Examples
– Pat relied on Chris does not entail Pat did something on Chris
– Pat put nuts in a cup does not entail Pat did something in a cup
– Pat slept until noon does entail Pat did something until noon
– Pat ate lunch at Bytes does entail Pat did something at Bytes
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 4
HPSGs
Agreement
• Two kinds so far (namely?)
• Both initially handled via stipulation in theHead-Specifier Rule
• But if we want to use this rule for categories that don’t have the AGR feature (such as PPs and APs, in English), we can’t build it into the rule.
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 5
HPSGs
The Specifier-Head Agreement Constraint (SHAC)
Verbs and nouns must be specified as:
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 6
HPSGsThe Count/Mass Distinction
• Partially semantically motivated– mass terms tend to refer to undifferentiated substances (air, butter,
courtesy, information)
– count nouns tend to refer to individuatable entities (bird, cookie, insult, fact)
• But there are exceptions:– succotash (mass) denotes a mix of corn & lima beans, so it’s not
undifferentiated.
– furniture, footwear, cutlery, etc. refer to individuatable artifacts with mass terms
– cabbage can be either count or mass, but many speakers get lettuce only as mass.
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 7
HPSGs – Semantics
The Linguist’s stance: Building a precise model• Some statements are statements about how the model works:
“[prep] and [AGR 3sing] cannot be combined because AGR is not a feature of the type prep.”
• Some statements are statements about how (we think) English or language in general works.
“The determiners a and many only occur with count nouns, the determiner
much only occurs with mass nouns, and the determiner the occurs with either.”
• Some are statements about how we code a particular linguistic fact within the model.
“All count nouns are [SPR < [COUNT +]>].”
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 8
HPSGs – Semantics
The Linguist’s stance:A Vista on the Set of Possible English Sentences
• ... as a background against which linguistic elements (words, phrases) have a distribution
• ... as an arena in which linguistic elements “behave” in certain ways
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 9
HPSGs - Semantics
So far, our “grammar” has no semantic representations. We have, however, been relying on semantic intuitions in our argumentation, and discussing semantic contrasts where they line up (or don't) with syntactic ones.
Examples?• structural ambiguity
• S/NP parallelism
• count/mass distinction
• complements vs. modifiers
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 10
HPSGs - Semantics
Aspects of meaning we won’t account for
• Pragmatics
• Fine-grained lexical semantics:
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 11
HPSGs - Semantics
Our Slice of a World of Meanings
“... the linguistic meaning of Chris saved Pat is a proposition that will be true just in case there is an actual situation that involves the saving of someone named Pat by someone named Chris.”
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 12
HPSGs - Semantics
Our Slice of a World of Meanings
What we are accounting for is the compositionality of
sentence meaning.
• How the pieces fit togetherSemantic arguments and indices
• How the meanings of the parts add up to the meaning of the whole.
Appending RESTR lists up the tree
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 13
HPSGs – Semantics in Constraint-based grammar
Constraints as generalized truth conditions• proposition: what must be the case for a proposition to be true
• directive: what must happen for a directive to be fulfilled
• question: the kind of situation the asker is asking about
• reference: the kind of entity the speaker is referring to
Syntax/semantics interface: Constraints on how syntactic arguments are related to semantic ones, and on how semantic information is compiled from different parts of the sentence.
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 14
HPSGs – Semantics – Feature Geometry
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 15
HPSGs – Semantics – How the pieces fit together
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 16
HPSGs – Semantics – How the pieces fit together
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 17
HPSGs – Semantics – How the pieces fit together
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 18
HPSGs – Semantics (pieces together)
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 19
HPSGs – Semantics (more detailed view of same tree)
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 20
HPSGs – Semantics
To Fill in Semantics for the S-node, we need the Semantics Principles
The Semantic Inheritance Principle:• In any headed phrase, the mother's MODE and INDEX are
identical to those of the head daughter.
The Semantic Compositionality Principle:• In any well-formed phrase structure, the mother's RESTR
value is the sum of the RESTR values of the daughter.
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 21
HPSGs – Semantics – semantics inheritance illustrated
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 22
HPSGs – Semantics - semantic compositionality illustrated
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 23
HPSGs – Semantics – what identifies indices
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 24
HPSGs – Semantics – summary wordscontribute predications
‘expose’ one index in those predications, for use by words or phrases
relate syntactic arguments to semantic arguments
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 25
HPSGs – Semantics – summary, grammar rules
identify feature structures (including the INDEX value) across daughters
Head Specifier
Rule
Head Complement
Rule
Head Modifier
Rule
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 26
HPSGs – Semantics – summary, grammar rulesidentify feature structures (including the INDEX value) across daughters
license trees which are subject to the semantic principles- SIP ‘passes up’ MODE and INDEX from head daughter
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 27
HPSGs – Semantics – summary, grammar rulesidentify feature structures (including the INDEX value) across daughters
license trees which are subject to the semantic principles
- SIP ‘passes up’ MODE and INDEX from head daughter
- SCP: ‘gathers up’ predications (RESTR list) from all daughters
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 28
HPSGs – other aspects of semantics
• Tense, Quantification (only touched on here)• Modification• Coordination• Structural Ambiguity
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 29
HPSGs – what were are trying to do
Objectives
• Develop a theory of knowledge of language
• Represent linguistic information explicitly enough to distinguish well-formed from ill-formed expressions
• Be parsimonious, capturing linguistically significant generalizations.
Why Formalize?
• To formulate testable predictions
• To check for consistency
• To make it possible to get a computer to do it for us
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 30
HPSGs –how we construct sentences
The Components of Our Grammar• Grammar rules
• Lexical entries
• Principles
• Type hierarchy (very preliminary, so far)
• Initial symbol (S, for now)
We combine constraints from these components.• Question: What says we have to combine them?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 31
HPSGs – an example
A cat slept.
• Can we build this with our tools?
• Given the constraints our grammar puts on well-formed sentences, is this one?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 32
HPSGs – lexical entry for “a”
• Is this a fully specified description?
• What features are unspecified? • How many word structures can this entry license?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 33
HPSGs – lexical entry for “cat”
• Which feature paths are abbreviated and Is this fully specified?
• What features are unspecified? • How many word structures can this entry license?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 34
HPSGs - Effect of Principles: the SHAC
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 35
HPSGs - Description of Word Structures for cat
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 36
HPSGs - Description of Word Structures for a
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 37
HPSGs - Building a Phrase
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 38
HPSGs - Constraints Contributed by Daughter Subtrees
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 39
HPSGs - Constraints Contributed by the Grammar Rule
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 40
HPSGs - A Constraint Involving the SHAC
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 41
HPSGs - Effects of the Valence Principle
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 42
HPSGs - Effects of the Head Feature Principle
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 43
HPSGs - Effects of the Semantic Inheritance Principle
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 44
HPSGs - Effects of the Semantic Compositionality Principle
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 45
HPSGs - Is the Mother Node Now Completely Specified?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 46
HPSGs - Lexical Entry for slept
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 47
HPSGs - Another Head-Specifier Phrase
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 48
HPSGs - Is this description fully specified?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 49
HPSGs - Does the top node satisfy the initial symbol?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 50
HPSGs - RESTR of the S node
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 51
HPSGs – Another example
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 52
HPSGs - Head Features from Lexical Entries
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 53
HPSGs - Head Features from Lexical Entries, plus HFP
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 54
HPSGs - Valence Features:Lexicon, Rules, and the Valence Principle
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 55
HPSGs - Required Identities: Grammar Rules
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 56
HPSGs - Two Semantic Features: the Lexicon & SIP
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 57
HPSGs - RESTR Values and the SCP
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 58
HPSGs - An Ungrammatical Example
What’s wrong with this sentence?
The Valence Principle, Head Specifier Rule
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 59
HPSGs – Overview
• Information movement in trees
• Exercise in critical thinking
• SPR and COMPS
• Technical details (lexical entries, trees)
• Analogies to other systems you might know, e.g., How is the type hierarchy like an ontology?
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 60
Statistical NLP – Introduction
NLP as we have examined thus far can be contrasted with statistical NLP. For example, statistical parsing researchers assue that there is a continuum and that the only distinction to be drawn is between the correct parse and all the rest.
The “parse” given by the parse tree on the
right would support this continuum view.
For statistical NLP researchers, there is no
Difference between parsing and syntactic
Disambiguation: its parsing all the way!
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 61
Statistical NLP – Statistical NLP is normally taught in 2 parts:• Part I lays out the mathematical and linguistic foundation that the other
parts build on. These include concepts and techniques normally referred to throughout the course.
• Part II covers word-centered work in Statistical NLP. There is a natural progression from simple to complex linguistic phenomena in collocations, n-gram models, word sense disambiguation, and lexical acquisition. This work is followed by techniques such as Markov Models, tagging, probabilistic context free grammars, and probabilistic parsing, which build on each other. Finally other applications and techniques are introduced: statistical alignment and machine translation, clustering, information retrieval, and text categorization.
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 62
Statistical NLP – What we will discuss
1. Information Retrieval and the Vector Space Model Typical IR system architecture, steps in document and query processing in IR, vector space model, tfidf - term frequency inverse document frequency weights, term weighting formula, cosine similarity measure, term-by-document matrix, reducing the number of dimensions, Latent Semantic Analysis, IR evaluation
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 63
Statistical NLP - – What we will discuss
2. Text Classification Text classification and text clustering, Types of text classification, evaluation measures in text classification, F-measure, Evaluation methods for classification: general issues - over fitting and under fitting, methods: 1. training error, 2. train and test, 3. n-fold cross-validation
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 64
Statistical NLP - – What we will discuss
3. Parser Evaluation, Text Clustering and CNG Classification Parser evaluation: PARSEVAL measures, labeled and unlabeled precision and recall, F-measure; Text clustering: task definition, the simple k-means method, hierarchical clustering, divisive and agglomerative clustering; evaluation of clustering: inter-cluster similarity, cluster purity, use of entropy or information gain; CNG -- Common N-Grams classification method
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 65
Statistical NLP - – What we will discuss
4. Probabilistic Modeling and Joint Distribution ModelElements of probability theory, Generative models, Bayesian inference, Probabilistic modeling: random variables, random configurations, computational tasks in probabilistic modeling, spam detection example, joint distribution model, drawbacks of joint distribution model
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 66
Statistical NLP - – What we will discuss
5. Fully Independent Model and Naive Bayes ModelFully independent model, example, computational tasks, sum-product formula; Naive Bayes model: motivation, assumption, computational tasks, example, number of parameters, pros and cons; N-gram model, language modeling in speech recognition
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 67
Statistical NLP - – What we will discuss
6. N-gram Model N-gram model: n-gram model assumption, graphical representation, use of log probabilities; Markov chain: stochastic process, Markov process, Markov chain; Perplexity and evaluation of N-gram models, Text classification using language models
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 68
Statistical NLP - – What we will discuss
7. Hidden Markov Model Smoothing: Add-one (Laplace) smoothing, Bell-Witten smoothing; Hidden Markov Model, graphical representations, assumption, HMM POS example, Viterbi algorithm -- use of dynamic programming in HMMs.
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 69
Statistical NLP - – What we will discuss
8. Bayesian Networks Bayesian Networks, definition, example, Evaluation tasks in Bayesian Networks: evaluation, sampling, inference in Bayesian Networks by brute force, general inference in Bayesian Networks is NP-hard, efficient inference in Bayesian Networks,
CSE6339 3.0 Introduction to Computational LinguisticsTuesdays, Thursdays 14:30-16:00 – South Ross 101
Fall Semester, 2011
Instructor: Nick Cercone - 3050 CSEB - [email protected] 70
Other Concluding Remarks
ATOMYRIADES
Nature, it seems, is the popular namefor milliards and milliards and milliardsof particles playing their infinite gameof billiards and billiards and billiards.