1 / 9

Frequency Estimates for Statistical Word Similarity Measures

Frequency Estimates for Statistical Word Similarity Measures. Egidio Terra and C.L.A. Clarke School of Computer Science University of Waterloo. Presenter: Cosmin Adrian Bejan. Introduction.

warren
Download Presentation

Frequency Estimates for Statistical Word Similarity Measures

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Frequency Estimates for Statistical Word Similarity Measures Egidio Terra and C.L.A. Clarke School of Computer Science University of Waterloo Presenter: Cosmin Adrian Bejan

  2. Introduction • A comparative study of two methods for estimating word cooccurence frequencies required by word similarity measures to solve human-oriented language tests. • Example of such tests: • determine the best synonym in a set of alternatives A={A1, A2, A3, A4} for a specific target word TW in a context C={w1’, w2’, … wn’} \ TW. • determine the best synonym when no context is available

  3. Measuring Word Similarity • the notion for cooccurence of two words can be depicted by a contingency table: • each dimension represents a random discrete variable Wi with range A = {wi,  wi}; • each cell represent the joint frequency where Nmax is the maximum number of cooccurences.

  4. Similarity between two words Pointwise Mutual Information Χ2- test Likelihood ratio Average Mutual Information

  5. Context supported similarity Cosine of Pointwise Mutual Information L1 norm Contextual Average Mutual Information Contextual Jensen- Shanon Digergence Pointwise Mutual Infor- mation of Multiple words

  6. Window-oriented approach • fw_i – frequency of wi • fw_1,w_2 – cooccurence frequency of w1 and w2 • N – size of the corpus in words • P(wi) = fw_i/N • fw_1,w_2 is estimated by the number of windows where the two words cooccur. • Nwt – number of windows of size t • P(w1, w2) = fw_1,w_2 / Nwt

  7. Document-oriented approach • dfw_i – frequency of a word wi. It corresponds to the number of documents in which the words appears. • D – the number of documents • P(wi) = dfw_i/ D • dfw_1,w_2 – cooccurence frequency of two words – is the number of documents where the words cooccur. • P(w1, w2) = dfw_1,w_2 / D

  8. Results for TOEFL test set

  9. Results for TS1 and context

More Related