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Sentence Similarity Based on Semantic Nets and Corpus Statistics

Sentence Similarity Based on Semantic Nets and Corpus Statistics. Author : Yuhua Li, David McLean, Zuhair A. Bandar, James D. O’Shea, and Keeley Crockett Reporter : Tze Ho-Lin 2006/1/3. TKDE, 2006. Outline. Motivation Objectives Methodology Evaluation Conclusion Personal Comments.

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Sentence Similarity Based on Semantic Nets and Corpus Statistics

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  1. Sentence Similarity Based on Semantic Nets and Corpus Statistics Author : Yuhua Li, David McLean, Zuhair A. Bandar, James D. O’Shea, and Keeley Crockett Reporter : Tze Ho-Lin 2006/1/3 TKDE, 2006

  2. Outline • Motivation • Objectives • Methodology • Evaluation • Conclusion • Personal Comments

  3. Motivation • Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. • These methods process sentences in a very high-dimensional space and are consequently • Inefficient • Require human input • Not adaptable to some application domains

  4. Objectives • Using lexical database to enable our method to model human common sense knowledge. • Incorporation of corpus statistics allows our method to be adaptable to different domains.

  5. Methodology (WordNet) (Brown Corpus)

  6. Evaluation Similarity correlation (Pearson)

  7. Conclusion • A method for measuring the semantic similarity based on semantic and word order information. • The proposed method provides similarity measures that are fairly consistent with human knowledge.

  8. Personal Comments • Application • Categorical data similarity measure. • Knowledge Retrieval • Advantage • This function has a simple, easy-to-implement formula. • Disadvantage • This technique compares words on a word-by-word basis. • The proposed method does not currently conduct word sense disambiguation for polysemous words.

  9. Method: Semantic Similarity between Words

  10. Method: Semantic Similarity between Sentences One element Is a word in the joint word set. Is its associated word in the sentence. n is the frequency of the word w in the corpus N is the total number of words in the corpus

  11. Method: Word Order Similarity between Sentences T1: RAM keeps things being worked with. T2: The CPU uses RAM as a short-term memory store. T={RAM keeps things being worked with The CPU uses as a short-term memory store}.

  12. Process for Deriving the Semantic Vector (preset threshold: 0.2) T1: RAM keeps things being worked with. T2: The CPU uses RAM as a short-term memory store. T={RAM keeps things being worked with The CPU uses as a short-term memory store}.

  13. Overall sentence similarity

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