More or less supervised supersense tagging of Twitter

We present two Twitter datasets annotated with coarse-grained word senses (supersenses), as well as a series of experiments with three learning scenarios for supersense tagging: weakly supervised learning, as well as unsupervised and supervised domain adaptation. We show that (a) off-the-shelf tools perform poorly on Twitter, (b) models augmented with embeddings learned from Twitter data perform much better, and (c) errors can be reduced using type-constrained inference with distant supervision from WordNet.

[1]  Yulia Tsvetkov,et al.  Cross-Lingual Metaphor Detection Using Common Semantic Features , 2013 .

[2]  Oren Etzioni,et al.  Named Entity Recognition in Tweets: An Experimental Study , 2011, EMNLP.

[3]  Gerhard Paass,et al.  Exploiting Semantic Constraints for Estimating Supersenses with CRFs , 2009, SDM.

[4]  George A. Miller,et al.  Using a Semantic Concordance for Sense Identification , 1994, HLT.

[5]  Josef van Genabith,et al.  From News to Comment: Resources and Benchmarks for Parsing the Language of Web 2.0 , 2011, IJCNLP.

[6]  Kemal Oflazer,et al.  Coarse Lexical Semantic Annotation with Supersenses: An Arabic Case Study , 2012, ACL.

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

[8]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[9]  Heng Ji,et al.  Analysis and Enhancement of Wikification for Microblogs with Context Expansion , 2012, COLING.

[10]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[11]  Yusuke Miyao,et al.  Learning with Lookahead: Can History-Based Models Rival Globally Optimized Models? , 2011, CoNLL.

[12]  Estimating Supersenses with Conditional Random Fields , 2008 .

[13]  Mary P. Harper,et al.  A Second-Order Hidden Markov Model for Part-of-Speech Tagging , 1999, ACL.

[14]  Yoav Freund,et al.  Large Margin Classification Using the Perceptron Algorithm , 1998, COLT' 98.

[15]  Rada Mihalcea,et al.  SenseLearner: Word Sense Disambiguation for All Words in Unrestricted Text , 2005, ACL.

[16]  Ben Taskar,et al.  Wiki-ly Supervised Part-of-Speech Tagging , 2012, EMNLP.

[17]  Yulia Tsvetkov,et al.  Augmenting English Adjective Senses with Supersenses , 2014, LREC.

[18]  John Langford,et al.  Search-based structured prediction , 2009, Machine Learning.

[19]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[20]  Wee Sun Lee,et al.  Learning Semantic Classes for Word Sense Disambiguation , 2005, ACL.

[21]  Eduard H. Hovy,et al.  ISI: Automatic Classification of Relations Between Nominals Using a Maximum Entropy Classifier , 2010, *SEMEVAL.

[22]  Yasemin Altun,et al.  Broad-Coverage Sense Disambiguation and Information Extraction with a Supersense Sequence Tagger , 2006, EMNLP.

[23]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[24]  Kalina Bontcheva,et al.  Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data , 2013, RANLP.

[25]  Timothy Baldwin,et al.  One Sense per Tweeter ... and Other Lexical Semantic Tales of Twitter , 2014, EACL.

[26]  Dirk Hovy,et al.  Learning part-of-speech taggers with inter-annotator agreement loss , 2014, EACL.

[27]  Abdelaziz Kriouile,et al.  Automatic word recognition based on second-order hidden Markov models , 1994, IEEE Trans. Speech Audio Process..

[28]  Timothy Baldwin,et al.  MELB-YB: Preposition Sense Disambiguation Using Rich Semantic Features , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[29]  Wee Sun Lee,et al.  Optimizing Classifier Performance in Word Sense Disambiguation by Redefining Sense Classes , 2007, IJCAI.

[30]  Mehmet Ali Yatbaz,et al.  The Noisy Channel Model for Unsupervised Word Sense Disambiguation , 2010, Computational Linguistics.