PPT-Acoustic Word Embeddings

Textual word embeddings map words to meaning and are thus based on semantics Different words can map to a similar location in the features space even though the letters composing the word are not the same. sengpielaudiocomZusammenhangDerAkustischenGroessenpdf Acoustic quantities v a ac Particle displacement ac Particle velocity ac Particle accelera tion UdK Berlin Sengpiel 092004 Schall ac Sound pressure vZ aZ cE ac PZ Sound intensity ac 22 Z vZ aZ Ec com Koray Kavukcuoglu DeepMind Technologies koraydeepmindcom Abstract Continuousvalued word embeddings learned by neural language models have re cently been shown to capture semantic and syntactic information about words very well setting performance Zou Richard Socher Daniel Cer Christopher D Manning Department of Electrical Engineering and Computer Science Department Stanford University Stanford CA 94305 USA wzoudanielcermanning stanfordedu richardsocherorg Abstract We introduce bilingual wor embeddings. encode about syntax?. Jacob Andreas and Dan Klein. UC Berkeley. Everybody loves word . embeddings. few. most. that. the. a. each. this. every. [. Collobert. 2011]. [. Collobert. 2011, . . Learning. for. . Word, Sense, Phrase, Document and Knowledge. Natural . Language Processing . Lab. , Tsinghua . University. Yu Zhao. , Xinxiong Chen, Yankai Lin, Yang Liu. Zhiyuan Liu. , Maosong Sun. p(D=1jw;c)=1 1+evw
vc wherevwandvc(eachad-dimensionalvector)arethemodelparameterstobelearned.Weseektomaximizethelog-probabilityoftheobservedpairsbelongingtothedata,leadingtotheobjective: argmaxvw;vcP Symbolic semantics,. DISTRIBUTIONAL SEMANTICS. Heng. . Ji. jih@rpi.edu. Oct13, 2015. Acknowledgement: distributional semantics slides from Omer Levy, . Yoav. Goldberg and . Ido. Dagan. Word Similarity & Relatedness. Ohio Center of Excellence in Knowledge-enabled Computing (. Kno.e.sis. ). Wright State University, Dayton, OH, USA. Amit Sheth. amit@knoesis.org. . . Derek Doran. derek@knoesis.org. . . Presented . 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 . Map nodes to low-dimensional . embeddings. .. 2) Graph neural networks. Deep learning architectures for graph-structured data. 3) Applications. Representation Learning on Networks, snap.stanford.edu/proj/embeddings-www, WWW 2018. What Is the Feature Vector . x. ?. Typically a vector representation of a single character or word. Often reflects the . context. in which that word is found. Could just do counts, but that leads to sparse vectors. @Weekly Meetup. 李博放. About me. Bofang Li 李 . 博放. . libofang@ruc.edu.cn. . http://bofang.stat-nba.com. . Renmin University of China . 中国人民大学. 09/2014-present. Ph.D. candidate. Julia Hirschberg. CS 4706. (Thanks . to Roberto . Pieraccini. and . Francis . Ganong. . for some slides). 2. Recreating the Speech Chain. DIALOG. SEMANTICS. SYNTAX. LEXICON. MORPHOLOGY. PHONETICS. VOCAL-TRACT. Why vector models of meaning?. computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How .