Download Ambiguity Resolution in Language Learning: Computational and by Hinrich Schütze PDF

By Hinrich Schütze

ISBN-10: 1575860759

ISBN-13: 9781575860756

This quantity is worried with how ambiguity and ambiguity answer are discovered, that's, with the purchase of different representations of ambiguous linguistic types and the information valuable for choosing between them in context. Schütze concentrates on how the purchase of ambiguity is feasible in precept and demonstrates that specific forms of algorithms and studying architectures (such as unsupervised clustering and neural networks) can be triumphant on the job. 3 forms of lexical ambiguity are taken care of: ambiguity in syntactic categorisation, semantic categorisation, and verbal subcategorisation. the amount offers 3 assorted types of ambiguity acquisition: Tag area, notice area, and Subcat Learner, and addresses the significance of ambiguity in linguistic illustration and its relevance for linguistic innateness.

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This generalization could not be exploited if left and right contexts were not treated separately. Another argument for the two-step derivation is that many words don't have any of the 250 most frequent words as their left or right neighbor. Hence, their context vectors are zero in the word-based scheme. The class-based scheme makes it more likely that meaningful representations are formed for all words in the vocabulary. The generalized context vectors were input to the tag induction procedure described above for word-based context vectors: 20,000 word triplets were selected from the corpus, encoded as 1,000-dimensional vectors (consisting of four generalized context vectors), decomposed by a singular value decomposition and clustered into 200 classes.

The justification of innate knowledge by Pinker and Lightfoot as a necessary remedy against lacking negative evidence applies to "because" as well as the null-subject parameter. I conclude that children can exploit implicit negative evidence in the form of nonoccurrence of forms and that innate knowledge is not necessary to make up for lacking negative evidence. Chapter 4 will discuss this point in more detail. In general, it is hard to show that syntax-specific innate knowledge is dispensable since it requires proof of the learnability of all of grammar, which is beyond the scope of this book.

It is an iterative procedure which starts with all words being separate one-member clusters, and then in each step merging the two clusters such that the average distance between members of the result cluster is minimal. Some of the vector sets that we will work with are too large for GAAC. In such cases the Buckshot algorithm offers an efficient way to "seed" a clustering using GAAC and then compute it for the full set of vectors. Buckshot selects a sample of vectors, clusters it with GAAC, and then extends it to all vectors by computing the centroid of each cluster and assigning vectors to the cluster whose centroid they are closest to.

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