This paper reports the results of Natural Language Processing (NLP) experiments in semantic parsing, based on a new semantic resource, the Pattern Dictionary of English Verbs (PDEV) (Hanks, 2013). This work is set in the DVC (Disambiguating Verbs by Collocation) project , a project in Corpus Lexicography aimed at expanding PDEV to a large scale. This project springs from a long-term collaboration of lexicographers with computer scientists which has given rise to the design and maintenance of specific, adapted, and user-friendly editing and exploration tools. Particular attention is drawn on the use of NLP deep semantic methods to help in data processing. Possible contributions of NLP include pattern disambiguation, the focus of this article. The present article explains how PDEV differs from other lexical resources and describes its structure in detail. It also presents new classification experiments on a subset of 25 verbs. The SVM model obtained a micro-average F1 score of 0.81.

In this paper we present an alignment experiment between patterns of verb use discovered by Corpus Pattern Analysis (CPA; Hanks 2004, 2008, 2012) and verb senses in OntoNotes (ON; Hovy et al. 2006, Weischedel et al. 2011). We present a probabilistic approach for mapping one resource into the other. Firstly we introduce a basic model, based on conditional probabilities, which determines for any given sentence the best CPA pattern match. On the basis of this model, we propose a joint source channel model (JSCM) that computes the probability of compatibility of semantic types between a verb phrase and a pattern, irrespective of whether the verb phrase is a norm or an exploitation. We evaluate the accuracy of the proposed mapping using cluster similarity metrics based on entropy.

There are a number of collocational constraints in natural languages that ought to play a more important role in natural language parsers. Thus, for example, it is hard for most parsers to take advantage of the fact that wine is typically drunk, produced, and sold, but (probably) not pruned. So too, it is hard for a parser to know which verbs go with which prepositions (e.g., set up) and which nouns fit together to form compound noun phrases (e.g., computer programmer). This paper will attempt to show that many of these types of concerns can be addressed with syntactic methods (symbol pushing), and need not require explicit semantic interpretation. We have found that it is possible to identify many of these interesting co-occurrence relations by computing simple summary statistics over millions of words of text. This paper will summarize a number of experiments carried out by various subsets of the authors over the last few years. The term collocation will be used quite broadly to include constraints on SVO (subject verb object) triples, phrasal verbs, compound noun phrases, and psychoiinguistic notions of word association (e.g., doctor/nurse).