Correlation-based Intrinsic Evaluation of Word Vector Representations

We introduce QVEC-CCA--an intrinsic evaluation metric for word vector representations based on correlations of learned vectors with features extracted from linguistic resources. We show that QVEC-CCA scores are an effective proxy for a range of extrinsic semantic and syntactic tasks. We also show that the proposed evaluation obtains higher and more consistent correlations with downstream tasks, compared to existing approaches to intrinsic evaluation of word vectors that are based on word similarity.

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

[2]  George A. Miller,et al.  A Semantic Concordance , 1993, HLT.

[3]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[4]  Yulia Tsvetkov,et al.  Metaphor Detection with Cross-Lingual Model Transfer , 2014, ACL.

[5]  Ramón Fernández Astudillo,et al.  Not All Contexts Are Created Equal: Better Word Representations with Variable Attention , 2015, EMNLP.

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

[7]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[8]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[9]  Felix Hill,et al.  SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation , 2014, CL.

[10]  Kenneth Ward Church,et al.  Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.

[11]  Ehud Rivlin,et al.  Placing search in context: the concept revisited , 2002, TOIS.

[12]  L. R. Moscovice Max Planck Institute for Evolutionary Anthropology, Department of Primatology , 2017 .

[13]  Manaal Faruqui,et al.  Community Evaluation and Exchange of Word Vectors at wordvectors.org , 2014, ACL.

[14]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

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

[16]  Sussi Olsen,et al.  Supersense tagging for Danish , 2015, NODALIDA.

[17]  Wanxiang Che,et al.  Revisiting Embedding Features for Simple Semi-supervised Learning , 2014, EMNLP.

[18]  Guillaume Lample,et al.  Evaluation of Word Vector Representations by Subspace Alignment , 2015, EMNLP.

[19]  Gemma Boleda,et al.  Distributional Semantics in Technicolor , 2012, ACL.

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

[21]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[22]  Angeliki Lazaridou,et al.  Fish Transporters and Miracle Homes: How Compositional Distributional Semantics can Help NP Parsing , 2013, EMNLP.

[23]  Wang Ling,et al.  Two/Too Simple Adaptations of Word2Vec for Syntax Problems , 2015, NAACL.

[24]  Noah A. Smith,et al.  Transition-Based Dependency Parsing with Stack Long Short-Term Memory , 2015, ACL.

[25]  Kevin Gimpel,et al.  Tailoring Continuous Word Representations for Dependency Parsing , 2014, ACL.