Syntactic category acquisition

1;01;11;21;31;4

1;51;6

daddy, mommybyedog, hi, uh ohbaby, ball, noeye, nose, banana, juice, shoe, kitty, bird, duck, car, book, balloon, bottle, night-night, woof, moo, ouch, baa baa, yum yumapple, cheese, ear, cracker, keys, bath, peekaboo, vroom, up, down, thatgrandpa, grandma, sock, hat, cat, fish, truck, boat, thank you, cup, spoon, back

Early words (Clark 2003)

Early words (Clark 2003)

• people daddy, mommy, baby• animals dog, kitty, bird, duck• body parts eye, nose, ear• food banana, juice, apple, cheese• toys ball, balloon, book• cloths shoe, sock, hat• vehicles car, truck, boat• household items bottle, keys, bath, spoon• routines bye, hi, uh oh, night-night, thank you, no• activities up, down, back• sound imitation woof, moo, ouch, baa baa, yum yum• deictics that

How do children learn syntactic

categories such as nouns, verbs, and

prepositions?

The meaning of syntactic categories

• Nouns typically denote objects, persons, animals

(nouns are non-relational and atemporal; Langacker)

• Verbs typically denote events and states

(verbs are relational and temporal; Langacker)

Cues for syntactic category acquisition

• Semantic cues (Gentner 1982; Pinker 1984)

• Pragmatic cues (Bruner 1975)

• Phonological cues (Monaghan et al. 2005)

• Distributional cues (Redington et al. 1998)

Maratsos and Chalkely (1980)

• Nouns: the __, X-s

• Verbs: will __, X-ing, X-ed,

Objections to distributional learning

Syntactic categories are commonly defined in terms of their distribution; thus, it cannot be a surprise that distributional information is informative about syntactic category status. The argument is trivial or even circular.

• ‘Noisy input data’

• Det Adj __ P N ….

Objections to distributional learning

The vast number of possible relationships that might be included in a distributional analysis is likely to overwhelm any distributional learning mechanism in a combinatorial explosion. (Pinker 1984)

• Distributional learning mechanisms do not search blindly for all possible relationships between linguistic items, i.e. the search is focused on specific distributional cues (Reddington et al. 1998).

Objections to distributional learning

The interesting properties of linguistic categories are abstract and such abstract properties cannot be detected in the input. (Pinker 1984)

• This assumption crucially relies on Pinker‘s particular view of grammar. If you take a construction grammar perspective, grammar (or syntax) is much more concrete (Redington et al. 1998).

Objections to distributional learning

Even if the child is able to determine certain correlations between distributional regularities and syntactic categories, this information is of little use because there are so many different cross-linguistic correlations that the child wouldn’t know which ones are relevant in his/her language. (Pinker 1984)

• Syntactic categories vary to some extent across languages (i.e. there are no fixed categories). Children recognize any distributional pattern regardless of the particular properties that categories in different languages may have (Redington et al. 1998)

Objections to distributional learning

Spurious correlations will occur in the input that will be misguiding. For instance, if the child hears

John eats meat.John eats slowly.The meat is good.

He may erroneously infer The slowly is good is a possible English sentence. (Pinker 1984)

• Children do not learn categories from isolated examples (Redington et al. 1998).

Redington et al. 1998 - Data

All adult speakers of the CHILDES database (2.5 million words).

Bigram statistics: Target words: 1000 most frequent words in the corpus Context words: 150 most frequent words in the corpus

Context size: 2 words preceding + 2 words following the target word:

x the __ of xin the __ x xwill have __ the x

Bigram statistics Context w. 1(the __ of)

Context w. 2(at the __ is)

Context w. 3(has __ him)

Context w. 4(He __ in)

Target w. 1Target w. 2Target w. 3Target w. 4Etc.

21037601

32191714

211078987

0512981398

Context vectors:Target word 1 210-321-2-0Target word 2 376-917-1-5Target word 3 0-1-1078-1298Target word 4 1-4-987-1398

Statistical analysis

• Hierarchical cluster analysis over context vectors:

dendogram

• Treatment of polysemous words

• ‘Slicing’ of the denogram

• Comparison of the clusters of the dendogram to a

‘benchmark’ (Collins Cobuild lexical dictionary)

Hierarchical cluster analysis

Result:

Local contexts have the strongest effect, notably the word

immediately preceding the target word is important.

Exp 1: Context size

"Learners might be innately biased towards considering

only these local contexts, whether as a result of limited

processing abilities (e.g. Elman 1993) or as a result of

language specific representational bias." (Redington et al.

1998)

Exp 2: Number of target words

Distributional learning is most efficient for high frequency

open class words.

Level of accuracy

Number of target words

Result:

nouns < verbs < function words

Exp 3: Category type

„Although content words are typically much less frequent,

their context is relatively predictable … Because there are

many more content words, the context of function words

will be relatively amaophous." (Redington et al. 1998)

Exp 4: Corpus size

Level of accuracy

Number of words

Result:

Including information about utterance boundaries

did not improve the level of accurarcy.

Exp 5: Utterance boundaries

Result:

The cluster analysis still revealed significant clusters,

but performance was much better when frequency

information was included.

Exp 6: Frequency vs occurrence

‘Frequency vectors’ were replaced by ‘occurrence vectors’:

Frequency vector Occurrence vector

27-0-12-0-0-12-2 1-0-1-0-0-1-1

0-213-2-1-45-3-0 0-1-1-1-1-1-0

Result:

The results decreased but were still significant.

Exp 7: Removing function words

Early child language includes very few function words.

Thus, Redington et al. removed all function words from the

context and repeated the cluster analysis without function

words.

Result:

Representing particular word classes through

discrete category labels (e.g. N), does not improve the

categorization of other categories (e.g. V).

Exp 8: Knowledge of word classes

The cluster analyses were performed over the distribution

of individual items. It is conceivable that the child

recognizes at some point discrete syntactic categories (cf.

semantic bootstrapping), which may facilitate the

categorization task.

Mintz et al. 2002. Cognitive Science

(1) The man [in the yellow car] …

(2) She [has not yet been] to NY.

1. Information about phrasal boundaries improves

performance.

2. Local contexts have the strongest effect (cf.

Redington et al. 1998).

3. The results for Ns are better than the results for Vs

(cf. Redington et al. 1998).

Monaghan et al. 2005. Cognition

(1) Nouns vs. verbs

(2) Open class vs. closed class.

1. Distributional information

2. Phonological information

Phonological features of syntactic

categories

1. Length Open class words are longer than

closed class words

2. Stress Closed class words usually do not

carry stress

3. Stress Nouns tend to be more often trochaic

than verbs (i.e. verbs are often iambic)

4. Consonants Closed class words have fewer

consonant cluster

5. Reduced vowels Closed class words include a higher

proportion of reduced vowels than

open class words

Phonological features of syntactic

categories

1. Interdentals Closed class words are more likely to

begin with an interdental fricative than

open class words

2. Nasals Nouns are more likely than verbs to

include nasals

3. Final voicing Nouns are more likely than verbs to

end in a voiced consonant

4. Vowel position Nouns tend to include more back

vowels than verbs

5. Vowel height The vowels of verbs tend to be higher

than the vowels of verbs

Results

Phonological features do not just reinforce distributional

information, but seem to be especially powerful in

domains in which distributional information is not so

easily available.

1. Distributional information is especially useful for

categorization of high frequency open class words.

2. Phonological information is more useful for catego-

rization of low frequency open class words (Zipf 1935).

3. Phonological information is also useful for the distinction

between open and closed class words.