A Supervised Model for Extraction of Multiword Expressions, Based on Statistical Context Features

Abstract

We present a method for extracting Multiword Expressions (MWEs) based on the immediate context they occur in, using a supervised model. We show some of these contextual features can be very discriminant and combining them with MWEspecific features results in a relatively accurate extraction. We define context as a sequential structure and not a bag of words, consequently, it becomes much more informative about MWEs.

Modeling the Non-Substitutability of Multiword Expressions with Distributional Semantics and a Log-Linear Model

“…Evert (2005) and Pecina (2010) study a wide range of association measures that can be employed to rank and classify collocations, respectively. assume that a word pair is a true MWE if the conditional probability of one word given the other is greater than the conditional probability of that word given synonyms of the other word, and Riedl and Biemann (2015), and Farahmand and Martins (2014) use contextual features to identify MWEs.…”

Never-Ending Multiword Expressions Learning

“…Ramisch et al (2008) use decision trees for classifying MWEs based on standard association measures as well, but they add variation entropy. In terms of classifiers, many alternatives have been tested like bayesian networks (Dubremetz and Nivre, 2014) and support vector machines (Farahmand and Martins, 2014). Zilio et al (2011) use a stable set of features, but compare several classification algorithms implemented in Weka.…”

“…SVM was the chosen classifier because it has presented good performance on diverse NLP tasks such as text categorization (Sassano, 2003), sentiment analysis (Mullen and Collier, 2004) and named entity recognition (Li et al, 2008), as well as standard corpus-based MWE extraction (Farahmand and Martins, 2014).…”

Investigating the Opacity of Verb-Noun Multiword Expression Usages in Context

“…These measures are broadly applied to identifying the types 1 of MWEs. While there is ongoing research to improve the type-based investigation of MWEs (Rondon et al, 2015;Farahmand and Martins, 2014;Salehi and Cook, 2013), the challenge of token-based identification of MWEs (as in tagging corpora for these expressions) requires more attention (Schneider et al, 2014;Brooke et al, 2014;Monti et al, 2015).…”