This paper describes the integration of verbal idioms into an Natural Language Processing (NLP) system, adopting a construction approach, which is based on the prior parsing stage, so that these MultiWord Expressions (MWE) can be taken into account in subsequent tasks, such as semantic role labeling or whole-part relation extraction. The paper focuses on body-part nouns, which are often part of...

Although MWE are relatively morphologically and syntactically fixed expressions, several types of flexibility can be observed in MWE, verbal MWE in particular. Identifying the degree of morphological and syntactic flexibility of MWE is very important for many Lexicographic and NLP tasks. Adding MWE variants/tokens to a dictionary resource requires characterizing the flexibility among other morp...

Over the past two decades or so, Multi-Word Expressions (MWEs; also called Multi-word Units) have been an increasingly important concern for Computational Linguistics and Natural Language Processing (NLP). The term MWE has been used to refer to various types of linguistic units and expressions, including idioms, noun compounds, phrasal verbs, light verbs and other habitual collocations. However...

Multiword Expressions (MWEs), a known nuisance for both linguistics and NLP, blur the lines between syntax and semantics. The semantic of a MWE cannot be expressed after combining the semantic of its constituents. In this study, we propose a novel approach called “semantic clustering” as an instrument for extracting the MWEs especially for resource constraint languages like Bengali. At the begi...

Multiword Expressions (MWEs) are one of the stumbling blocks for more precise Natural Language Processing (NLP) systems. Particularly, the lack of coverage of MWEs in resources can impact negatively on the performance of tasks and applications, and can lead to loss of information or communication errors. This is especially problematic in technical domains, where a significant portion of the voc...

In this article we present a novel algorithm for the task of comprehensively segmenting texts into MWEs. With the basis for this algorithm (referred to as text partitioning) being recently developed, these results constitute its first performance-evaluated application to a natural language processing task. A differentiating feature of this single-parameter model is its focus on gap (i.e., punct...

We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium. We concentrate in particular on the Dependency Minimal Recursion Semantics (DMRS) formalism, a graph-based representation designed for compositional semantic representation with deep grammars. Our main focus is on English, and sp...

In the recent past, the important role played by multiword expressions in the language has been recognized by the natural language processing community. Simply put, a multiword expression (MWE) is a word collocation that exhibits markedly peculiar linguistic behaviour in terms of lexicalization, syntax or semantics. Among others, ubiquitous compound nouns, idioms and phrasal verbs fall into thi...

An impressive amount of work was devoted over the past few decades to collocation extraction. The state of the art shows that there is a sustained interest in the morphosyntactic preprocessing of texts in order to better identify candidate expressions; however, the treatment performed is, in most cases, limited (lemmatization, POS-tagging, or shallow parsing). This article presents a collocatio...

Due to the formal variability and the irregular behaviour of MWEs on different levels of linguistic description, they are a potential source of errors for many NLP applications, e.g. Machine Translation. While most of the known approaches to MWE identification focus on one dimension of irregular behaviour, we present an approach that combines morpho-syntactic features (extracted from dependency...