PPT-Distributional models

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 . The ARMApq series is generated by 12 pt pt 12 qt 949 949 949 Thus is essentially the sum of an autoregression on past values of and a moving average o tt t white noise process Given together with starting values of the whole series 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 fig, olive, plum, pineapple, strawberry, etc. and asked subjects to rate on a 7-point scale how good an example each member was of its category. The results showed a clear trend of category gradednes While economic instruments are often seen as being particularly regressive, other policy instruments may have equivalent (or worse) effects The focus of much work on the distributional impacts of envi 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. Thisarticleconsiderstheproblemofassessingthedistributionalconsequencesofatreatmentonsomeoutcomevariableofinterestwhentreatmentintakeis(possibly)nonrandomized,butthereisabinaryinstrumentavailableforthe 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 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 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. 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. in Lexical Typology:. Constructing a typological questionnaire. Daria . Ryzhova. School of Linguistics. NRU HSE. Outline. Lexical Typology: Frame-based Approach. Ideology. Typological questionnaire. Omer Levy. . Ido. Dagan. Bar-. Ilan. University. Israel. Steffen Remus Chris . Biemann. Technische. . Universität. Darmstadt. Germany. Lexical Inference. Lexical Inference: Task Definition. 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. .