On Collocations and Their Interaction with Parsing and Translation

Abstract

We address the problem of automatically processing collocations—a subclass of multi-word expressions characterized by a high degree of morphosyntactic flexibility—in the context of two major applications, namely, syntactic parsing and machine translation. We show that parsing and collocation identification are processes that are interrelated and that benefit from each other, inasmuch as syntactic information is crucial for acquiring collocations from corpora and, vice versa, collocational information can be used to improve parsing performance. Similarly, we focus on the interrelation between collocations and machine translation, highlighting the use of translation information for multilingual collocation identification, as well as the use of collocational knowledge for improving translation. We give a panorama of the existing relevant work, and we parallel the literature surveys with our own experiments involving a symbolic parser and a rule-based translation system. The results show a significant improvement over approaches in which the corresponding tasks are decoupled

Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers

“…Research in NLP has already addressed a number of collocation-related tasks, in particular: (1) collocation error detection, categorization, and correction in writings of second language learners (Ferraro et al, 2011;Wanner et al, 2013;Ferraro et al, 2014;Rodríguez-Fernández et al, 2015); (2) creation of collocation-enriched lexical resources Maru et al, 2019;Di Fabio et al, 2019); (3) use of knowledge on collocations in downstream NLP tasks, among them, e.g., machine translation (Seretan, 2014), word sense disambiguation (Maru et al, 2019), natural language generation (Wanner and Bateman, 1990), or semantic role labeling (Scozzafava et al, 2020); (4) probes involving collocations for understanding to which extent language models are able to identify non-compositional meanings (Shwartz and Dagan, 2019;Garcia et al, 2021); and (5) detection and categorization of collocations with respect to their semantics (Wanner et al, 2006;Espinosa Anke et al, 2019;Levine et al, 2020;Espinosa-Anke et al, 2021). It is this last task which is the focus of this paper.…”

Evaluating language models for the retrieval and categorization of lexical collocations

“…Section 3.1. Modeling, recognizing, and classifying collocations in corpora has obvious applications for automatically creating and expanding lexicographic resources, as well as for various downstream NLP applications, among them, e.g., machine translation (Seretan, 2014), word sense disambiguation (Maru et al, 2019), or natural language generation (Wanner and Bateman, 1990). The two main contributions of this paper thus are:…”

Idiomatic or literal? Production of collocations in German read speech

“…Collocations and idiomatic expressions can be considered as a special case of words co-occurring with a higher frequency than expected by chance. They have received considerable interest by various researchers (e.g., [3,19,20,6,21,22,23,9,24,16,25,26]). Collocations and idiomatic expressions exhibit characteristics that make them a prime field of study for the effects of frequency of usage on speech production and perception.…”

“…Collocations are difficult to model but crucial for successful natural language processing systems [26]. They show patterns that make them stand out compared to the standard usage of words.…”