Detection of Verbal Multi-Word Expressions via Conditional Random
            Fields with Syntactic Dependency Features and Semantic Re-Ranking

Maldonado, Alfredo; Han, Lifeng; Moreau, Erwan; Alsulaimani, Ashjan; Chowdhury, Koel Dutta; Vogel, Carl; Li, Qun

doi:10.18653/v1/w17-1715

Cited by 19 publications

(15 citation statements)

References 20 publications

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“…In the PARSEME Shared Tasks, several machine learning-based methods competed on the four languages under investigation, among others. Some of them relied on parsing (Al Saied, Constant, and Candito 2017;Nerima, Foufi, and Wehrli 2017;Simkó, Kovács, and Vincze 2017;Waszczuk 2018), whereas others exploited sequence labeling using CRFs (Boroş et al 2017;Maldonado et al 2017;Moreau et al 2018) and neural networks (Klyueva, Doucet, and Straka 2017;Berk et al 2018;Boroş and Burtica 2018;Ehren, Lichte, and Samih 2018;Stodden, QasemiZadeh, and Kallmeyer 2018;Zampieri et al 2018).…”

Section: Methods For Identifying Lvcsmentioning

confidence: 99%

Detecting light verb constructions across languages

Nagy¹,

Rácz

Vincze

2019

Nat. Lang. Eng.

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Light verb constructions (LVCs) are verb and noun combinations in which the verb has lost its meaning to some degree and the noun is used in one of its original senses, typically denoting an event or an action. They exhibit special linguistic features, especially when regarded in a multilingual context. In this paper, we focus on the automatic detection of LVCs in raw text in four different languages, namely, English, German, Spanish, and Hungarian. First, we analyze the characteristics of LVCs from a linguistic point of view based on parallel corpus data. Then, we provide a standardized (i.e., language-independent) representation of LVCs that can be used in machine learning experiments. After, we experiment on identifying LVCs in different languages: we exploit language adaptation techniques which demonstrate that data from an additional language can be successfully employed in improving the performance of supervised LVC detection for a given language. As there are several annotated corpora from several domains in the case of English and Hungarian, we also investigate the effect of simple domain adaptation techniques to reduce the gap between domains. Furthermore, we combine domain adaptation techniques with language adaptation techniques for these two languages. Our results show that both out-domain and additional language data can improve performance. We believe that our language adaptation method may have practical implications in several fields of natural language processing, especially in machine translation.

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Section: Methods For Identifying Lvcsmentioning

confidence: 99%

Detecting light verb constructions across languages

Nagy¹,

Rácz

Vincze

2019

Nat. Lang. Eng.

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“…Some systems that participated in edition 1.0 of the PARSEME Shared Task are also based on parsing (Al Saied et al, 2017;Nerima et al, 2017;Simkó et al, 2017). Other approaches to MWE identification include sequence labeling using CRFs (Boroş et al, 2017;Maldonado et al, 2017) and neural networks (Klyueva et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Book reviews

2020

Yearbook of Phraseology

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“…Previous work studied the impact of external lexicons (Riedl and Biemann, 2016) and of several feature sets (Maldonado et al, 2017) on CRFs for MWE identification. Character-based embeddings have been shown useful to predict MWE compositionality out of context (Hakimi Parizi and Cook, 2018).…”

Section: Related Workmentioning

confidence: 99%

The Impact of Word Representations on Sequential Neural MWE Identification

Zampieri¹,

Ramisch²,

Damnati³

2019

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

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Recent initiatives such as the PARSEME shared task have allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural verbal MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based embeddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lemmas, depending on the morphological complexity of the language.

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Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking

Cited by 19 publications

References 20 publications

Detecting light verb constructions across languages

Detecting light verb constructions across languages

Book reviews

The Impact of Word Representations on Sequential Neural MWE Identification

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