PPT-Word Meaning and Similarity

Word Similarity Distributional Similarity I Problems with thesaurusbased meaning We dont have a thesaurus for every language Even if we do they have problems with recall M any . 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 . Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining . their . similarities (or dissimilarities). . Multi-label Protein Subcellular Localization. Shibiao WAN and Man-Wai MAK. The Hong Kong Polytechnic University. Sun-Yuan KUNG. Princeton University. Outline. Introduction and Motivation. Retrieval of GO Terms. Theory and Applications. Danai Koutra (CMU). Tina Eliassi-Rad (Rutgers) . Christos Faloutsos (CMU). SDM 2014. , Friday April 25. th. 2014, Philadelphia, PA. Who we are. Danai Koutra, CMU. Node and graph similarity,. -. based Clustering. Mohammad. . Rezaei. , Pasi Fränti. rezaei@cs.uef.fi. Speech. and . Image. . Processing. . Unit. University of Eastern Finland. . August 2014. Keyword-Based Clustering. An object such as a text document, website, movie and service can be described by a set of keywords. these theories have explanatory power domains partially role of relational judgments. Previous structural and aspects of notion of relational similarity by the fact that and her some ways there is in Focus on . word and sentence . similarity. Formal side: define similarity in principle. Characterizing word meaning . in context. Given a word in a particular sentence context: Can we characterize its meaning without reference to dictionary senses?. Word Senses. . and Word Relations. Reminder: 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. a Multi-Layered Indexing Approach. Yongjiang Liang, . Peixiang Zhao. CS @ FSU. zhao@cs.fsu.edu. Outline. Introduction. State-of-the-art solutions. ML-Index & similarity search. Experiments. Conclusion. Story #1 – Rope Burn. h. umiliation . hu. -mil-i-. ation. fringes . frin-ges. expectations ex-. pect. -a-. tions. coaxed coax-. ed. sincere sin-. cere. hesitating . hes. -i-ta-ting. Warm Up. Solve each proportion.. 1.. . . 2.. . 3.. 4. . If . ∆. QRS . ~ . ∆. XYZ. , identify the pairs of congruent angles and write 3 proportions using pairs of corresponding sides.. . computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How . tall. . is Mt. Everest?”. Candidate A: “The . Presenter. : Monica Farkash. Bryan Hickerson. . mfarkash@us.ibm.com. . bhickers@us.ibm.com. . 2. Outline. The challenge: Providing a subset from a regression test suite. Our new Jaccard/K-means (JK) approach . Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse.