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By Hinrich Schütze

This quantity is worried with how ambiguity and ambiguity solution are realized, that's, with the purchase of different representations of ambiguous linguistic kinds and the information helpful for choosing between them in context. Schütze concentrates on how the purchase of ambiguity is feasible in precept and demonstrates that exact varieties of algorithms and studying architectures (such as unsupervised clustering and neural networks) can be triumphant on the activity. 3 different types of lexical ambiguity are handled: ambiguity in syntactic categorisation, semantic categorisation, and verbal subcategorisation. the amount provides 3 various versions of ambiguity acquisition: Tag house, note house, and Subcat Learner, and addresses the significance of ambiguity in linguistic illustration and its relevance for linguistic innateness.

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Proximity in the space corresponds to proximity in syntactic function. For example, transitive and intransitive verbs are close to each other, whereas verbs and nouns are distant. the who returns 300 75 sleep 133 200 FIGURE 2 Distributional matrix for the construction of (left) syntactic context vectors. Figures 2 and 3 show a simple example of how to represent words in such a space (the numbers are not from an actual corpus, but were made up for ease of presentation). , it tells us how often the strings "the returns" and "who returns" occurred in the corpus.

The tag adjective stands for both adnominal and predicative uses, for example, the uses of "black" in (27). (27) a. the black cat (adnominal) b. The cat is black, (predicative) In a preprocessing step, the Penn Treebank parses of the Brown corpus were used to determine whether a token functions as an adnominal modifier. Adjectives and participles were classified as ADN if immediately dominated by an expansion of a noun, and as PRD, VBN, and VBG if immediately dominated by an expansion of a verb.

Here is one way one could evaluate distributional part-of-speech clustering with respect to the Brown tags, assuming there are 30 major tags. • Cluster all tokens into 30 clusters. • Measure accuracy as the percentage of token pairs that satisfy Inference 26b. • Measure discrimination as the percentage of token pairs that satisfy Inference 26a. Notice that 100% accuracy can be trivially achieved by assigning all tokens to one cluster. 100% discrimination can be achieved by assigning each token to a different cluster.

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