Formal description of multi-word lexemes with the finite-state formalism IDAREX

Breidt, Elisabeth; Segond, Frédérique; Valetto, Giuseppe

doi:10.3115/993268.993360

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…Rules can be as simple as direct matching, but can also use more sophisticated, context-sensitive constraints encoded as finite-state transducers for instance. Historically, rules based on finite-state transducers offered a simple generic framework to deal with variability, discontiguity, and ambiguity (Gross 1989;Breidt, Segond, and Valetto 1996). Identification methods for contiguous MWEs such as open compounds are generally based on dictionaries compiled into finite-state transducers.…”

Section: Rule-based Methodsmentioning

confidence: 99%

“…Another approach comprises two processing stages: morphological analysis of simple words followed by a composition of regular rules to identify MWEs, as in Oflazer, Çetinoglu, and Say (2004) for Turkish. Breidt, Segond, and Valetto (1996) design regular rules that handle morphological variations and restrictions like the French idiom perdre ADV* :la :tête (lit. lose ADV* :the :head, 'to lose one's mind'), 16 lexical and structural variations (birth date = date of birth).…”

Section: Rule-based Methodsmentioning

confidence: 99%

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Multiword Expression Processing: A Survey

Constant

Eryiğit

Monti

et al. 2017

Computational Linguistics

165

126

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Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by “MWE processing,” distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives.

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Section: Rule-based Methodsmentioning

confidence: 99%

Section: Rule-based Methodsmentioning

confidence: 99%

Multiword Expression Processing: A Survey

Constant

Eryiğit

Monti

et al. 2017

Computational Linguistics

165

126

View full text Add to dashboard Cite

show abstract

“…More elaborate are approaches based on finitestate-related formalisms. They usually indicate the morphological categories and features of individual MWE components, and offer rule-based combinatorial description of their variability patterns (Karttunen et al, 1992;Breidt et al, 1996;Oflazer et al, 2004;Silberztein, 2005;Krstev et al, 2010;Al-Haj et al, 2014;Lobzhanidze, 2017;Czerepowicka and Savary, 2018). They mostly cover continuous (e.g.…”

Section: Lexicons Of Mwesmentioning

confidence: 99%

Without lexicons, multiword expression identification will never fly: A position statement

Savary¹,

Cordeiro²,

Ramisch³

2019

Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

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Because most multiword expressions (MWEs), especially verbal ones, are semantically non-compositional, their automatic identification in running text is a prerequisite for semantically-oriented downstream applications. However, recent developments, driven notably by the PARSEME shared task on automatic identification of verbal MWEs, show that this task is harder than related tasks, despite recent contributions both in multilingual corpus annotation and in computational models. In this paper, we analyse possible reasons for this state of affairs. They lie in the nature of the MWE phenomenon, as well as in its distributional properties. We also offer a comparative analysis of the state-of-the-art systems, which exhibit particularly strong sensitivity to unseen data. On this basis, we claim that, in order to make strong headway in MWE identification, the community should bend its mind into coupling identification of MWEs with their discovery, via syntactic MWE lexicons. Such lexicons need not necessarily achieve a linguistically complete modelling of MWEs' behavior, but they should provide minimal morphosyntactic information to cover some potential uses, so as to complement existing MWE-annotated corpora. We define requirements for such a minimal NLP-oriented lexicon, and we propose a roadmap for the MWE community driven by these requirements.

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“…Any natural language processing (NLP) system needs to address the issue of handling multiword expressions, including Phrasal Verbs (PV) [Sag et al 2002;Breidt et al 1996]. This paper presents a proven approach to identifying English PVs based on pattern matching using a formalism called Expert Lexicon.…”

Section: Introductionmentioning

confidence: 99%

An expert lexicon approach to identifying English phrasal verbs

Zhang

Niu

Jiang

et al. 2003

Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03

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Phrasal Verbs are an important feature of the English language. Properly identifying them provides the basis for an English parser to decode the related structures. Phrasal verbs have been a challenge to Natural Language Processing (NLP) because they sit at the borderline between lexicon and syntax. Traditional NLP frameworks that separate the lexicon module from the parser make it difficult to handle this problem properly. This paper presents a finite state approach that integrates a phrasal verb expert lexicon between shallow parsing and deep parsing to handle morpho-syntactic interaction. With precision/recall combined performance benchmarked consistently at 95.8%-97.5%, the Phrasal Verb identification problem has basically been solved with the presented method.

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Formal description of multi-word lexemes with the finite-state formalism IDAREX

Cited by 10 publications

References 4 publications

Multiword Expression Processing: A Survey

Multiword Expression Processing: A Survey

Without lexicons, multiword expression identification will never fly: A position statement

An expert lexicon approach to identifying English phrasal verbs

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