Disambiguation of Verbal Shifters

Negation is an important contextual phenomenon that needs to be addressed in sentiment analysis. Next to common negation function words, such as not or none, there is also a considerably large class of negation content words, also referred to as shifters, such as the verbs diminish, reduce or reverse. However, many of these shifters are ambiguous. For instance, spoil as in spoil your chance reverses the polarity of the positive polar expression chance while in spoil your loved ones, no negation takes place. We present a supervised learning approach to disambiguating verbal shifters. Our approach takes into consideration various features, particularly generalization features.

[1]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[2]  Dietrich Klakow,et al.  A survey on the role of negation in sentiment analysis , 2010, NeSp-NLP@ACL.

[3]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[4]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[5]  Olga Babko-Malaya,et al.  Different Sense Granularities for Different Applications , 2004, HLT-NAACL 2004.

[6]  Danqi Chen,et al.  A Fast and Accurate Dependency Parser using Neural Networks , 2014, EMNLP.

[7]  Sanda M. Harabagiu,et al.  Negation, Contrast and Contradiction in Text Processing , 2006, AAAI.

[8]  Yoshua Bengio,et al.  Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.

[9]  Daniel Jurafsky,et al.  Learning to Merge Word Senses , 2007, EMNLP.

[10]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[11]  Massimo Poesio,et al.  Negation of protein-protein interactions: analysis and extraction , 2007, ISMB/ECCB.

[12]  Mitchell P. Marcus,et al.  OntoNotes: The 90% Solution , 2006, NAACL.

[13]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[14]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[15]  Robert L. Mercer,et al.  Class-Based n-gram Models of Natural Language , 1992, CL.

[16]  Michael Wiegand,et al.  Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features , 2017, IJCNLP.

[17]  Iryna Gurevych,et al.  Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization , 2016, ACL.

[18]  Janyce Wiebe,et al.  Subjectivity Word Sense Disambiguation , 2009, EMNLP.