PPT-Components for a semantic textual similarity system

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. . 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. medical dictations. Stefan Petrik . , . Christina . Drexel, . Leo Fessler . , . Jeremy Jancsary . , . Alexandra Klein . ,Gernot . Kubin . , . Johannes Matiasek . , . Franz Pernkopf . , . Harald . Trost. Ciro . Cattuto. , Dominik Benz, Andreas . Hotho. , . Gerd. . Stumme. Presented by. Smitashree. . Choudhury. Overview. Motivation. Measures of . semantic Relatedness. Semantic . Grounding of measures. １２月７日. 研究会. 祭都援炉. (. マットエンロ. ). Up until now: Getting to know NLP. “Speech and Language Processing” (. Jurafsky. & Martin). 論文：. On-Demand Information Extract . 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 . Randy . Goebel. Alberta Innovates Centre for Machine Learning. Department of Computing Science. University of Alberta. Edmonton, Alberta . Canada. rgoebel@ualberta.ca. Fuji-san. BIRS. Science or Engineering?. Nikhil Johri. CS 224N. 1. Motivating Questions. What is the value added from academic collaboration? . Division of labor?. Mixture of individual contributions?. New, synergistic ideas?. Can we identify different collaboration styles?. 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. of Binaries. Yaniv David. , Nimrod . Partush. , . Eran. . Yahav. . @. *. The research leading to these results has received funding . from the . European Union's - Seventh Framework . Programme. (FP7) under grant agreement n° . Lioma. Lecture . 18: Latent Semantic Indexing. 1. Overview. Latent semantic indexing . Dimensionality reduction. LSI in information retrieval. 2. Outline. Latent semantic indexing . Dimensionality reduction. Deduplication o f large amounts of code Romain Keramitas FOSDEM 2019 Clones def foo(name: str): print('Hello World, my name is ' + name) def bar(name: str): print('Hello World, my name is {}'.format(name)) Deduplication o f large amounts of code Romain Keramitas FOSDEM 2019 Clones def foo(name: str): print('Hello World, my name is ' + name) def bar(name: str): print('Hello World, my name is {}'.format(name)) 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. Xiangen . Hu. CCNU & . UoM. Agenda. Introduction. Basic semantic comparison techniques. Examples . of semantic spaces. A general framework. A few . applications. Hands-. on . （. if time permits).