Fish Transporters and Miracle Homes: How Compositional Distributional Semantics can Help NP Parsing

In this work, we argue that measures that have been shown to quantify the degree of semantic plausibility of phrases, as obtained from their compositionally-derived distributional semantic representations, can resolve syntactic ambiguities. We exploit this idea to choose the correct parsing of NPs (e.g., (live fish) transporter rather than live (fish transporter)). We show that our plausibility cues outperform a strong baseline and significantly improve performance when used in combination with state-of-the-art features.

[1]  Kenneth Ward Church,et al.  Using Web-scale N-grams to Improve Base NP Parsing Performance , 2010, COLING.

[2]  Eva Maria Vecchi,et al.  (Linear) Maps of the Impossible: Capturing Semantic Anomalies in Distributional Space , 2011 .

[3]  Mark Lauer,et al.  Corpus Statistics Meet the Noun Compound: Some Empirical Results , 1995, ACL.

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

[5]  Christina L. Gagné,et al.  Constituent integration during the processing of compound words: Does it involve the use of relational structures? , 2009 .

[6]  David Vadas.  Large-Scale Supervised Models for Noun Phrase Bracketing , 2007 .

[7]  Stephen Clark,et al.  Mathematical Foundations for a Compositional Distributional Model of Meaning , 2010, ArXiv.

[8]  Georgiana Dinu,et al.  DISSECT - DIStributional SEmantics Composition Toolkit , 2013, ACL.

[9]  James R. Curran,et al.  Parsing Noun Phrases in the Penn Treebank , 2011, Computational Linguistics.

[10]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

[11]  Remko Scha,et al.  Learning from errors: Using vector-based compositional semantics for parse reranking , 2013, CVSM@ACL.

[12]  Michael Collins,et al.  Head-Driven Statistical Models for Natural Language Parsing , 2003, CL.

[13]  Frank Keller,et al.  The Web as a Baseline: Evaluating the Performance of Unsupervised Web-based Models for a Range of NLP Tasks , 2004, NAACL.

[14]  Andrew Y. Ng,et al.  Parsing with Compositional Vector Grammars , 2013, ACL.

[15]  Dan Klein,et al.  Web-Scale Features for Full-Scale Parsing , 2011, ACL.

[16]  James R. Curran,et al.  Adding Noun Phrase Structure to the Penn Treebank , 2007, ACL.

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

[18]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

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

[20]  Patrick Pantel,et al.  From Frequency to Meaning: Vector Space Models of Semantics , 2010, J. Artif. Intell. Res..

[21]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[22]  Dan Klein,et al.  Learning Accurate, Compact, and Interpretable Tree Annotation , 2006, ACL.

[23]  Preslav Nakov,et al.  Search Engine Statistics Beyond the n-Gram: Application to Noun Compound Bracketing , 2005, CoNLL.