SemEval-2013 Task 5: Evaluating Phrasal Semantics

This paper describes the SemEval-2013 Task 5: “Evaluating Phrasal Semantics”. Its first subtask is about computing the semantic similarity of words and compositional phrases of minimal length. The second one addresses deciding the compositionality of phrases in a given context. The paper discusses the importance and background of these subtasks and their structure. In succession, it introduces the systems that participated and discusses evaluation results.

[1]  Leila Kosseim,et al.  ClaC: Semantic Relatedness of Words and Phrases , 2013, *SEMEVAL.

[2]  Mirella Lapata,et al.  Composition in Distributional Models of Semantics , 2010, Cogn. Sci..

[3]  Iryna Gurevych,et al.  Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary , 2008, LREC.

[4]  Wai Lam,et al.  Evaluation Challenges in Large-Scale Document Summarization , 2003, ACL.

[5]  Helmut Schmidt,et al.  Probabilistic part-of-speech tagging using decision trees , 1994 .

[6]  Sophia Ananiadou,et al.  Developing a Robust Part-of-Speech Tagger for Biomedical Text , 2005, Panhellenic Conference on Informatics.

[7]  Christian Wartena.  HsH: Estimating Semantic Similarity of Words and Short Phrases with Frequency Normalized Distance Measures , 2013, SemEval@NAACL-HLT.

[8]  Thierry Poibeau,et al.  Latent Vector Weighting for Word Meaning in Context , 2011, EMNLP.

[9]  E. Guevara.  A Regression Model of Adjective-Noun Compositionality in Distributional Semantics , 2010 .

[10]  Rafael Muñoz,et al.  UMCC_DLSI-(EPS): Paraphrases Detection Based on Semantic Distance , 2013, *SEMEVAL.

[11]  Silvia Bernardini,et al.  The WaCky wide web: a collection of very large linguistically processed web-crawled corpora , 2009, Lang. Resour. Evaluation.

[12]  Tim Van de Cruys,et al.  MELODI: Semantic Similarity of Words and Compositional Phrases using Latent Vector Weighting , 2013, SemEval@NAACL-HLT.

[13]  Alexander F. Gelbukh,et al.  UNAL: Discriminating between Literal and Figurative Phrasal Usage Using Distributional Statistics and POS tags , 2013, SemEval@NAACL-HLT.

[14]  Mirella Lapata,et al.  Vector-based Models of Semantic Composition , 2008, ACL.

[15]  Marco Baroni,et al.  Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space , 2010, EMNLP.

[16]  Ioannis Korkontzelos,et al.  Estimating Linear Models for Compositional Distributional Semantics , 2010, COLING.

[17]  Caroline Fenlon,et al.  IIRG: A Naive Approach to Evaluating Phrasal Semantics , 2013, SemEval@NAACL-HLT.

[18]  Marco Baroni,et al.  Morph-it! A free corpus-based morphological resource for the Italian language , 2005 .

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.