PPT-Improving Distributional Similarity

with Lessons Learned from Word Embeddings Presented by Jiaxing Tan Some Slides from the original paper presentation 1 Outline Background Hyperparameter to experiment Experiment and Result. se 1 Introduction Distributional approaches to meaning acquisition utilize distributional proper ties of linguistic entities as the building blocks of semantics In doing so they rely fundamentally on a set of assumptions about the nature of language 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 Association and Similarity. Ido Dagan. Including Slides by:. . Katrin. Erk (mostly), Marco Baroni,. Alessandro Lenci (BLESS). 2. Word Association Measures. Goal: measure the statistical strength of word (term) co-occurrence in corpus. Lee Department of Computer Science Cornell University Ithaca, NY 14853-7501 cornell, edu We study distributional similarity measures for the purpose of improving probability estima- tion for unseen c 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 Katrin Erk. University of Texas at . Austin. Meaning in Context Symposium. München. September 2015. Joint work with Gemma . Boleda. Semantic features by example: . Katz & Fodor. Different meanings of a word characterized by lists of semantic features. Harris T. Lin. , . Sanghack. Lee, . Ngot. Bui and . Vasant. . Honavar. Artificial Intelligence Research Laboratory. Department of Computer Science. Iowa State University. htlin@iastate.edu. Introduction. 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 . Word Association and Similarity. Ido Dagan. Including Slides by:. . Katrin. Erk (mostly), Marco Baroni,. Alessandro Lenci (BLESS). 2. Word Association Measures. Goal: measure the statistical strength of word (term) co-occurrence in corpus. Katrin . Erk. You can get an idea of what a word means from observing it in context. He filled the . wampimuk. , passed it around, and we all drank some. We found a little hairy . wampimuk. . sleeping behind a tree. . S. imilarity to Semantic Relations. Georgeta. . Bordea. , November 25. Based on a talk by Alessandro . Lenci. . titled “Will DS ever become Semantic?”, Jan 2014. Distributional Semantics . (DS. with Lessons Learned from Word Embeddings. Omer Levy. . . Yoav. Goldberg . Ido. Dagan. Bar-. Ilan. University. Israel. 1. Word Similarity & Relatedness. How similar is . pizza. to . 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. Erk. You can get an idea of what a word means from observing it in context. He filled the . wampimuk. , passed it around, and we all drank some. We found a little hairy . wampimuk. . sleeping behind a tree. .