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.
Read or Download Ambiguity Resolution in Language Learning: Computational and Cognitive Models PDF
Similar semantics books
Essentially the most enigmatic features of expertise issues time. due to the fact that pre-Socratic occasions students have speculated concerning the nature of time, asking questions corresponding to: what's time? the place does it come from? the place does it move? The significant inspiration of The constitution of Time is that point, at base, constitutes a phenomenologically actual adventure.
In Taking Scope, Mark Steedman considers the syntax and semantics of quantifier scope in interplay with negation, polarity, coordination, and pronominal binding, between different buildings. The semantics is "surface compositional," in that there's a direct correspondence among syntactic kinds and operations of composition and kinds and compositions on the point of logical shape.
The final subject of this e-book is the improvement of a “realistic” version of which means; it has to account for the ecological foundation of that means in belief, motion, and interplay, and is sensible within the feel of “scientific realism”, i. e. it truly is in response to the main profitable paradigm of contemporary technological know-how: dynamical platforms idea.
- Semantics of Specification Languages (SoSL): Proceedings of the International Workshop on Semantics of Specification Languages, Utrecht, The Netherlands, 25 – 27 October 1993
- Lexical Competence (Language, Speech, and Communication)
- Formal syntax and semantics of programming languages : a laboratory based approach
- The Meaning of Topic and Focus: The 59th Bridge Street Accent
Extra info for Ambiguity Resolution in Language Learning: Computational and Cognitive Models
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.