Animacy Acquisition Using Morphological Case

Animacy is an inherent property of entities that nominals refer to in the physical world. This semantic property of a nominal has received much attention in both linguistics and computational linguistics. In this paper, we present a robust unsupervised technique to infer the animacy of nominals in languages with rich morphological case. The intuition behind our method is that the control/agency of a noun depicted by case marking can approximate its animacy. A higher control over an action implies higher animacy. Our experiments on Hindi show promising results withF andPurity scores of 89 and 86 respectively.

[1]  Samuel R. Bowman,et al.  Automatic Animacy Classification , 2012, HLT-NAACL.

[2]  Gosse Bouma,et al.  Automatic animacy classication , 2013 .

[3]  Seppo Kittilä,et al.  Case, animacy and semantic roles , 2011 .

[4]  Vito Pirrelli,et al.  Climbing the Path to Grammar: A Maximum Entropy Model of Subject/Object Learning , 2005, ACL 2005.

[5]  Chris Brew,et al.  Multilingual Animacy Classification by Sparse Logistic Regression , 2010 .

[6]  Miriam Butt,et al.  The Dative-Ergative Connection , 2006 .

[7]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[8]  Miriam Butt,et al.  The redevelopment of Indo-Aryan case systems from a lexical semantic perspective , 2011 .

[9]  R. Harald Baayen,et al.  Predicting the dative alternation , 2007 .

[10]  Lilja Øvrelid,et al.  Towards Robust Animacy Classification Using Morphosyntactic Distributional Features , 2006, EACL.

[11]  R. J. Evans,et al.  NP Animacy Identification for Anaphora Resolution , 2007, J. Artif. Intell. Res..

[12]  Judith Aissen,et al.  Differential Object Marking: Iconicity vs. Economy , 2003 .

[13]  Suzanne Stevenson,et al.  Automatic Verb Classification Based on Statistical Distributions of Argument Structure , 2001, CL.

[14]  W. Bruce Croft.  Typology and Universals , 1990 .

[15]  Tara Warrier Mohanan,et al.  Arguments in Hindi , 1990 .

[16]  Fei Xia,et al.  A Multi-Representational and Multi-Layered Treebank for Hindi/Urdu , 2009, Linguistic Annotation Workshop.

[17]  Colin P. Masica.  The Indo-Aryan Languages , 1991 .

[18]  Richard Evans,et al.  Learning to identify animate references , 2001, CoNLL.

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Constantin Orasan,et al.  Improving anaphora resolution by identifying animate entities in texts , 2002 .

[21]  Sander Lestrade,et al.  Animacy, argument structure, and argument encoding , 2008 .

[22]  Joakim Nivre,et al.  When word order and part-of-speech tags are not enough - Swedish dependency parsing with rich linguistic features , 2007 .

[23]  Holly P. Branigan,et al.  Contributions of animacy to grammatical function assignment and word order during production , 2008 .

[24]  Lilja Øvrelid,et al.  Empirical Evaluations of Animacy Annotation , 2009, EACL.

[25]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[26]  Bharat Ram Ambati,et al.  Two semantic features make all the difference in Parsing accuracy , 2008 .

[27]  C. MogotsiI..  Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze , 2010 .

[28]  Bernard Comrie,et al.  Language Universals and Linguistic Typology: Syntax and Morphology , 1981 .

[29]  S. Thompson,et al.  Transitivity in Grammar and Discourse , 1980 .