A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations

Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.

[1]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[2]  Peter D. Turney.  Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL , 2001, ECML.

[3]  Tony Veale,et al.  WordNet Sits the S.A.T. - A Knowledge-Based Approach to Lexical Analogy , 2004, ECAI.

[4]  C. Burgess,et al.  Semantic and associative priming in the cerebral hemispheres: Some words do, some words don't … sometimes, some places , 1990, Brain and Language.

[5]  Michael L. Littman,et al.  Corpus-based Learning of Analogies and Semantic Relations , 2005, Machine Learning.

[6]  Charles L. A. Clarke,et al.  Efficiency vs. Effectiveness in Terabyte-Scale Information Retrieval , 2005, TREC.

[7]  D. Rothstein.  Cognition and Thought: An Information-Processing Approach. , 1966 .

[8]  Yves Lepage,et al.  Purest ever example-based machine translation: Detailed presentation and assessment , 2005, Machine Translation.

[9]  Peter D. Turney.  Similarity of Semantic Relations , 2006, CL.

[10]  Preslav Nakov,et al.  SemEval-2007 Task 04: Classification of Semantic Relations between Nominals , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[11]  John A. Carroll,et al.  Applied morphological processing of English , 2001, Natural Language Engineering.

[12]  U. Neisser,et al.  Cognition and thought : an information-processing approach , 1966 .

[13]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[14]  Graeme Hirst,et al.  Lexical chains as representations of context for the detection and correction of malapropisms , 1995 .

[15]  Jeffrey P. Bigham,et al.  Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems , 2003, ArXiv.

[16]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[17]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[18]  Michael Lesk,et al.  Word-word associations in document retrieval systems , 1969 .

[19]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[20]  Yves Lepage,et al.  Solving Analogies on Words: An Algorithm , 1998, COLING-ACL.

[21]  Brian Falkenhainer,et al.  The Structure-Mapping Engine: Algorithm and Examples , 1989, Artif. Intell..

[22]  Ming Zhou,et al.  Identifying Synonyms among Distributionally Similar Words , 2003, IJCAI.

[23]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[24]  G. Reeke.  Marvin Minsky, The Society of Mind , 1991, Artif. Intell..

[25]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[26]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[27]  Barbara Rosario,et al.  Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy , 2001, EMNLP.