PPT-NLP Text Similarity Thesaurus-based Word Similarity Methods

Author : brianna | Published Date : 2022-06-11

Quiz Which pair of words exhibits the greatest similarity 1 Deerelk 2 Deerhorse 3 Deermouse 4 Deerroof Quiz Answer Which pair of words exhibits the greatest similarity

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NLP Text Similarity Thesaurus-based Word Similarity Methods: Transcript


Quiz Which pair of words exhibits the greatest similarity 1 Deerelk 2 Deerhorse 3 Deermouse 4 Deerroof Quiz Answer Which pair of words exhibits the greatest similarity 1 Deerelk 2 Deerhorse. Similarity. David Kauchak. CS159 Fall 2014. Admin. Assignment 5 out. Word similarity. How similar are two words?. sim(w. 1. , w. 2. ) = . ?. ?. score:. rank:. w. w. 1. w. 2. w. 3. list: w. 1. . and . Word Senses. . and Word Relations. Terminology: lemma and . wordform. A . lemma. or . citation . form. Same . stem, part of speech, rough . semantics. A . wordfor. m. The inflected word as it appears in text. Set 10. Lexical Relations. s. lides adapted from Dan . Jurafsky. and Bill . MacCartney. Three. levels of . m. eaning. Lexical . Semantics (words). Sentential . / Compositional / Formal Semantics. Discourse . David Kauchak. cs160. Fall . 2009. Administrative. Hw5/paper review. what . would be useful for the . authors. technical. , refers to not only the results, but the rest of the . paper. give . as many specific examples as . Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. WordNet. Lubomir. . Stanchev. Example . Similarity Graph. Dog. Cat. 0.3. 0.3. Animal. 0.8. 0.2. 0.8. 0.2. Applications. If we type . automobile. . in our favorite Internet search engine, for example Google or Bing, then all top results will contain the word . Word . Similarity: . Distributional Similarity (I). Problems with thesaurus-based . meaning. We don’t have a thesaurus for every language. Even if we do, . they have problems with . recall. M. any . Jing . Zhang, . Jie. . Tang . , Cong . Ma . , . Hanghang. . Tong . , Yu . Jing . , and . Juanzi. . Li. Presented by Moumita Chanda Das . Outline. Introduction. Problem formulation. Panther using path sampling. CSE, HKUST. March 20. Recap. String declaration. str1=“Hong”. str2=“Kong”. String Operators. strr. =str1+str2. “H” in . strr. String Slicing. strr. [. i. ]. strr. [:. i. ]. strr. [. i. :]. David Kauchak. cs458. Fall 2012. Administrative. Schedule. Readings. Lunch today!. HW4 due tomorrow. Attendance. Today’s class. Blend of introductory material and research talk. Problem of topic segmentation. CS159 . Fall . 2014. Admin. Assignment 4. Quiz #2 Thursday. Same . rules as quiz #1. First 30 minutes of class. Open book and . notes. Assignment 5 out on Thursday. Quiz #2. Topics. Linguistics 101. Parsing. Dan Jurafsky. Stanford University. Introduction and Course Overview. What is Computational Lexical Semantics. Any computational process involving word meaning!. Computing . Word Similarity . Distributional (Vector) Models of . Word Senses. . and Word Relations. Terminology: lemma and . wordform. A . lemma. or . citation . form. Same . stem, part of speech, rough . semantics. A . wordfor. m. The inflected word as it appears in text. Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing.

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