MultiMWE: building a multi-lingual multi-word expression (MWE) parallel corpora

Multi-word expressions (MWEs) are a hot topic in research in natural language processing (NLP), including topics such as MWE detection, MWE decomposition, and research investigating the exploitation of MWEs in other NLP fields such as Machine Translation. However, the availability of bilingual or multi-lingual MWE corpora is very limited. The only bilingual MWE corpora that we are aware of is from the PARSEME (PARSing and Multi-word Expressions) EU project. This is a small collection of only 871 pairs of English-German MWEs. In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora. Our collections are 3,159,226 and 143,042 bilingual MWE pairs for German-English and Chinese-English respectively after filtering. We examine the quality of these extracted bilingual MWEs in MT experiments. Our initial experiments applying MWEs in MT show improved translation performances on MWE terms in qualitative analysis and better general evaluation scores in quantitative analysis, on both German-English and Chinese-English language pairs. We follow a standard experimental pipeline to create our MultiMWE corpora which are available online. Researchers can use this free corpus for their own models or use them in a knowledge base as model features

Designing a Russian Idiom-Annotated Corpus

This paper describes the development of an idiom-annotated corpus of Russian. The corpus is compiled from freely available resources online and contains texts of different genres. The idiom extraction, annotation procedure, and a pilot experiment using the new corpus are outlined in the paper. Considering the scarcity of publicly available Russian annotated corpora, the corpus is a much-needed resource that can be utilized for literary and linguistic studies, pedagogy as well as for various Natural Language Processing tasks

Indirectly Named Entity Recognition

[EN] We define here indirectly named entities, as a term to denote multiword expressions referring to known named entities by means of periphrasis.  While named entity recognition is a classical task in natural language processing, little attention has been paid to indirectly named entities and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of indirectly named entities in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.This research has been funded by the FEDER (Fonds européen de développement régional) and selected by the French-Swiss programme Interreg V. We would like to thank Claire Wuillemin for her preliminary work in the DecRIPT project about the State-of-the-Art in NER and SER in 2020. We would also like to thank for their advice Gilles Falquet, Luka Nerima, Eric Wehrli and Jean-Philippe Goldman at the University of Geneva.Kauffmann, A.; Rey, F.; Atanassova, I.; Gaudinat, A.; Greenfield, P.; Madinier, H.; Cardey, S. (2021). Indirectly Named Entity Recognition. Journal of Computer-Assisted Linguistic Research. 5(1):27-46. https://doi.org/10.4995/jclr.2021.15922OJS274651Abney, Steven. 1987. "The English Noun Phrase in its Sentential Aspect." PhD diss., Massachusetts Institute of Technology.Alsharaf, H., S. Cardey, P. Greenfield, D. Limame, and I. Skouratov. 2003. "Fixedness, the complexity and fragility of the phenomenon: some solutions for natural language processing." In Proceedings of ICL17. Prague, Czech Republic: Matfyzpress.Ananthanarayanan, Rema, Vijil Chenthamarakshan, Prasad M Deshpande, and Raghuram Krishnapuram. 2008. 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Sun, and Qika Lin. 2020. "Jointly Optimized Neural Coreference Resolution with Mutual Attention." In Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, Texas, USA: Association for Computing Machinery. https://doi.org/10.1145/3336191.3371787Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. Baltimore, Maryland: Association for Computational Linguistics. https://doi.org/10.3115/v1/P14-5010Martin, Louis, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Benoıt Sagot, and Djamé Seddah. 2020. "Les modèles de langue contextuels CamemBERT pour le français: impact de la taille et de l'hétérogénéité des données d'entrainement (CamemBERT Contextual Language Models for French: Impact of Training Data Size and Heterogeneity)" [in French]. In Actes de la 6e conférence conjointe Journées d'Etudes sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Etudiants Chercheurs en Informatique pour le' Traitement Automatique des Langues (RECITAL, 22e édition). Volume 2: Traitement Automatique des Langues Naturelles, 54-65. Nancy, France: ATALA et AFCP.Mitkov, Ruslan. 2014. Anaphora resolution. Routledge. https://doi.org/10.4324/9781315840086Mohamed, Muhidin A., and Mourad Chabane Oussalah. 2020. "A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics." Language Resources and Evaluation 54 : 457-485. https://doi.org/10.1007/s10579-019-09466-4Nadeau, David, and Satoshi Sekine. 2007. "A survey of named entity recognition and classification." Lingvisticae Investigationes 30: 3-26. https://doi.org/10.1075/li.30.1.03nadNayel, Hamada A., H. L. Shashirekha, Hiroyuki Shindo, and Yuji Matsumoto. 2019. "Improving Multi-Word Entity Recognition for Biomedical Texts." CoRRabs/1908.05691. arXiv:1908.05691.Nebhi, Kamel. 2013. "Named Entity Disambiguation using Freebase and Syntactic Parsing." In [email protected], Damien, Maud Ehrmann, and Sophie Rosset. 2016. "Evaluating Named Entity Recognition." Chap. 6 in Named Entities for Computational Linguistics, 111-129. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119268567.ch6Ortiz Suarez, Pedro Javier, Yoann Dupont, Benjamin Muller, Laurent Romary, and Benoıt Sagot. 2020. "Establishing a New State-of-the-Art for French Named Entity Recognition" [in English]. In Proceedings of the 12th Language Resources and Evaluation Conference, 4631-4638. Marseille, France: European Language Resources Association.Petit, Gérard. 2006. "Le nom de marque déposée : nom propre, nom commun et terme." Meta 51, no. 4: 690-705. doi:10.7202/014335ar. https://doi.org/10.7202/014335arQu, Meng, Xiang Ren, and Jiawei Han. 2017. "Automatic Synonym Discovery with Knowledge Bases." In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 997-1005. KDD '17. Halifax, NS, Canada: Association for Computing Machinery. https://doi.org/10.1145/3097983.3098185Racicot, André. 2009. "Traduire le monde: Venise du Nord et autres surnoms." L'Actualité langagière, vol. 6, n° 2, 23. Travaux publics et Services gouvernementaux Canada.Rey, François-Claude, and Kauffmann Alexis. 2021. "French indirectly named entities (version 1.3) [Data set]." Zenodo. https://doi.org/10.5281/zenodo.5158253.Rosales-Méndez, Henry, Aidan Hogan, and Barbara Poblete. 2019. "Fine-Grained Evaluation for Entity Linking." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 718-727. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1066Sales, Juliano Efson, André Freitas, Brian Davis, and Siegfried Handschuh. 2016. "A Compositional-Distributional Semantic Model for Searching Complex Entity Categories." In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, 199-208. Berlin, Germany: Association for Computational Linguistics. https://doi.org/10.18653/v1/S16-2025Schmitt, X., S. Kubler, J. Robert, M. Papadakis, and Y. LeTraon. 2019. "A Replicable Comparison Study of NER Software: StanfordNLP, NLTK, OpenNLP, SpaCy, Gate." 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The lexico-phraseology of THE and A/AN in spoken English: a corpus-based study

The English articles (THE, A, AN) are normally described in terms of the grammar of the language. This is only natural, since they are extremely frequent, fit into certain well-defined syntactic slots, and usually help to communicate only very broad aspects of textual meaning. However, as John Sinclair has pointed out (1999, pp.160-161), the articles are also found as components of many lexico-phraseological units, and in such cases a normal grammatical description may not be of relevance. An example he gives is the presence of A in the phrase 'come to a head', where ‘A has little more status than that of a letter of the alphabet’ (p.161). Sinclair also makes the observation that, ‘I do not know of an estimate of the proportion of instances of A, for example, that are not a realisation of the choice of article but of the realisation of part of a multi-word expression.’ (p.161). The present paper addresses the questions raised by Sinclair, and does so with reference to both the definite and the indefinite article. It focuses, in particular, on the spoken language, and presents the results of analyses of random samples of the articles in the spoken component of the British National Corpus (hereafter BNC-spkn). According to the data in Leech et al (2001, p.144), THE is the most frequent word in BNC-spkn and A is the sixth most frequent (a rank position which remains unaltered when the frequencies of A and AN are combined). Using the BNCweb interface, and specifying that the relevant word forms should be ‘articles’, the total numbers of tokens are: an 19,049; a 200,004; the 409,060. Since the numbers are very high, the samples investigated also contained a reasonably large number of tokens (500). The relative samples corresponded to the following proportions of tokens in BNC-spkn: an 2.62%, a 0.25%, the 0.12%. The latter two are very low percentages, and for this reason, three separate samples of each were investigated, in order to see the extent to which the samples differed. Analysis of article usage was carried out in the first instance by reading right-sorted concordance lines. Whenever doubts arose, larger contexts were retrieved from the corpus. Various reference works were also consulted, including Berry (1993), Francis et al (1998), and various corpus-based dictionaries and grammars. The data presented includes: description of the various types of lexico-phraseological unit found; the proportions of the samples judged to involve the different lexico-phraseological phenomena identified; the problems encountered when deciding whether or not phraseology is an important factor in specific instances of article usage; and the number of tokens in each sample which were in some way irrelevant, for example because they involved speaker repetition of the article, or the non-completion of a noun phrase

Formulaic language

The notion of formulaicity has received increasing attention in disciplines and areas as diverse as linguistics, literary studies, art theory and art history. In recent years, linguistic studies of formulaicity have been flourishing and the very notion of formulaicity has been approached from various methodological and theoretical perspectives and with various purposes in mind. The linguistic approach to formulaicity is still in a state of rapid development and the objective of the current volume is to present the current explorations in the field. Papers collected in the volume make numerous suggestions for further development of the field and they are arranged into three complementary parts. The first part, with three chapters, presents new theoretical and methodological insights as well as their practical application in the development of custom-designed software tools for identification and exploration of formulaic language in texts. Two papers in the second part explore formulaic language in the context of language learning. Finally, the third part, with three chapters, showcases descriptive research on formulaic language conducted primarily from the perspectives of corpus linguistics and translation studies. The volume will be of interest to anyone involved in the study of formulaic language either from a theoretical or a practical perspective

Theories and methods

The notion of formulaicity has received increasing attention in disciplines and areas as diverse as linguistics, literary studies, art theory and art history. In recent years, linguistic studies of formulaicity have been flourishing and the very notion of formulaicity has been approached from various methodological and theoretical perspectives and with various purposes in mind. The linguistic approach to formulaicity is still in a state of rapid development and the objective of the current volume is to present the current explorations in the field. Papers collected in the volume make numerous suggestions for further development of the field and they are arranged into three complementary parts. The first part, with three chapters, presents new theoretical and methodological insights as well as their practical application in the development of custom-designed software tools for identification and exploration of formulaic language in texts. Two papers in the second part explore formulaic language in the context of language learning. Finally, the third part, with three chapters, showcases descriptive research on formulaic language conducted primarily from the perspectives of corpus linguistics and translation studies. The volume will be of interest to anyone involved in the study of formulaic language either from a theoretical or a practical perspective

PARSEME Survey on MWE Resources

International audienceThis paper summarizes the first results of an ongoing survey on multiword resources carried out within the IC1207 Cost ActionPARSEME (PARSing and Multi-word Expressions). Despite the availability of language resource catalogues and the inventory ofmultiword data-sets available at the SIGLEX-MWE website, multiword resources are scattered and prove to be difficult to be found.In many cases, language resources such as corpora, treebanks or lexical databases include multiwords as part of their data or take theminto consideration in their annotations. However, it is needed to centralize these resources so that other researches may subsequentlyuse them. The final aim of this survey is thus to create a portal where researchers may find multiword resources or multiword-awarelanguage resources for their research. We report on how the survey was designed and analyze the data gathered so far. We also discussthe problems we have detected upon examination of the data and possible ways of enhancing the survey

Exploiting multilingual lexical resources to predict MWE compositionality

Semantic idiomaticity is the extent to which the meaning of a multiword expression (MWE) cannot be predicted from the meanings of its component words. Much work in natural language processing on semantic idiomaticity has focused on compositionality prediction, wherein a binary or continuous-valued compositionality score is predicted for an MWE as a whole, or its individual component words. One source of information for making compositionality predictions is the translation of an MWE into other languages. This chapter extends two previously-presented studies – Salehi & Cook (2013) and Salehi et al. (2014) – that propose methods for predicting compositionality that exploit translation information provided by multilingual lexical resources, and that are applicable to many kinds of MWEs in a wide range of languages. These methods make use of distributional similarity of an MWE and its component words under translation into many languages, as well as string similarity measures applied to definitions of translations of an MWE and its component words. We evaluate these methods over English noun compounds, English verb-particle constructions, and German noun compounds. We show that the estimation of compositionality is improved when using translations into multiple languages, as compared to simply using distributional similarity in the source language. We further find that string similarity complements distributional similarity

New perspectives on cohesion and coherence: Implications for translation

The contributions to this volume investigate relations of cohesion and coherence as well as instantiations of discourse phenomena and their interaction with information structure in multilingual contexts. Some contributions concentrate on procedures to analyze cohesion and coherence from a corpus-linguistic perspective. Others have a particular focus on textual cohesion in parallel corpora that include both originals and translated texts. Additionally, the papers in the volume discuss the nature of cohesion and coherence with implications for human and machine translation.The contributors are experts on discourse phenomena and textuality who address these issues from an empirical perspective. The chapters in this volume are grounded in the latest research making this book useful to both experts of discourse studies and computational linguistics, as well as advanced students with an interest in these disciplines. We hope that this volume will serve as a catalyst to other researchers and will facilitate further advances in the development of cost-effective annotation procedures, the application of statistical techniques for the analysis of linguistic phenomena and the elaboration of new methods for data interpretation in multilingual corpus linguistics and machine translation