syntactic category acquisition
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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.
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