Normalized Google Distance for Collocation Extraction from Islamic Domain

This study investigates the properties of Arabic collocations, and classifies them according to their structural patterns on Islamic domain. Based on linguistic information, the patterns and the variation of the collocations have been identified.  Then, a system that extracts the collocations from Islamic domain based on statistical measures has been described. In candidate ranking, the normalized Google distance has been adapted to measure the associations between the words in the candidates set. Finally, the n-best evaluation that selects n-best lists for each association measure has been used to annotate all candidates in these lists manually. The following association measures (log-likelihood ratio, t-score, mutual information, and enhanced mutual information) have been utilized in the candidate ranking step to compare these measures with the normalized Google distance in Arabic collocation extraction. In the experiment of this work, the normalized Google distance achieved the highest precision value 93% compared with other association measures. In fact, this strengthens our motivation to utilize the normalized Google distance to measure the relatedness between the constituent words of the collocations instead of using the frequency-based association measures as in the state-of-the-art methods. Keywords: normalized Google distance, collocation extraction, Islamic domai

Multiword expressions at length and in depth

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

Assessing English language learners' collocation knowledge:A systematic review of receptive and productive measurements

Since collocation knowledge is integral to second language vocabulary depth, it necessitates a careful examination of various measurement approaches. To this end, the current paper provides an overview and evaluation of extant collocation measurements used in empirical studies on L2 English (N = 153) published between 1980 and 2023 indexed in the SSCI, SCIE, AHCI, SCOPUS, and ERIC databases. Six instruments, seven item formats, and three other assessment tools were identified and reviewed for the assessment of receptive and productive collocation knowledge. The review focused on the collocation knowledge measured by each tool, the instrument and/or item format employed, item design, reported reliability, and potential drawbacks of employing each instrument and item format in research or practice. The review proposes several theoretical and practical considerations for future assessments of and research on English collocation knowledge.</p

Discovering multiword expressions

In this paper, we provide an overview of research on multiword expressions (MWEs), from a natural lan- guage processing perspective. We examine methods developed for modelling MWEs that capture some of their linguistic properties, discussing their use for MWE discovery and for idiomaticity detection. We con- centrate on their collocational and contextual preferences, along with their fixedness in terms of canonical forms and their lack of word-for-word translatatibility. We also discuss a sample of the MWE resources that have been used in intrinsic evaluation setups for these methods

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. "Rule Based Synonyms for Entity Extraction from Noisy Text." In Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data AND '08, 31-38. Singapore: Association for Computing Machinery. https://doi.org/10.1145/1390749.1390756Bachellier, Jean-Louis. 1972. "Sur-Nom." Le texte: de la théorie à la recherche, no. 19: 69-92. doi :10.3406/comm.1972.1283. https://doi.org/10.3406/comm.1972.1283Baldwin, Timothy, and Su Nam Kim. 2013. "Multiword Expressions." In Handbook of Natural Language Processing, Second Edition, edited by Nitin Indurkhya and Fred J. Damerau, 267-292. Boca Raton, USA: CRCPress.Bohn, C., and Kjeti Nørvag. 2010. "Extracting Named Entities and Synonyms from Wikipedia." In Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, 1300-1307. https://doi.org/10.1109/AINA.2010.50Cai, Desheng, and Gongqing Wu. 2019. "Content-aware attributed entity embedding for synonymous named entity discovery." Neurocomputing 329: 237-247. https://doi.org/10.1016/j.neucom.2018.10.055Chakrabarti, K., S. Chaudhuri, T. Cheng, and Dong Xin. 2012. "A framework for robust discovery of entity synonyms." In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1384-1392, Beijing, China: Association for Computing Machinery. https://doi.org/10.1145/2339530.2339743Charton, Eric, Michel Gagnon, and Benoit Ozell. 2011. "Génération automatique de motifs de détection d'entités nommées en utilisant des contenus encyclopédiques (Automatic generation of named entity detection patterns using encyclopedic contents)" [in French]. In Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs, 13-24. Montpellier, France: ATALA.Cho, Hyejin, Wonjun Choi, and Hyunju Lee. 2017. "A method for named entity normalization in biomedical articles: application to diseases and plants." BMC bioinformatics 18, no. 1 ( 1-12. https://doi.org/10.1186/s12859-017-1857-8Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186. Minneapolis, Minnesota: Association for Computational Linguistics.Friburger, Nathalie. 2006. "Linguistique et reconnaissance automatique des noms propres." Meta 51, no. 4: 637-650. doi:10.7202/014331ar. https://doi.org/10.7202/014331arGuenoune, Hani, Kevin Cousot, Mathieu Lafourcade, Melissa Mekaoui, and Cédric Lopez. 2020. "A Dataset for Anaphora Analysis in French Emails." In Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, 165-175. Barcelona, Spain (online): Association for Computational Linguistics.Honnibal, Matthew, and Ines Montani. 2017. "spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing."Kampeera, Wannachai, and Sylviane Cardey-Greenfield. 2012. "Building a Lexically and Semantically-Rich Resource for Paraphrase Processing." In Advances in Natural Language Processing, edited by Hitoshi Isahara and Kyoko Kanzaki, 138-143. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_14Kauffmann, Alexis. 2013. "Structural Asymmetries in Machine Translation: The case of English-Japanese". PhD diss., Université de Genève. https://doi.org/10.13097/archive-ouverte/unige:34540.Lample, Guillaume, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. "Neural Architectures for Named Entity Recognition." In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 260-270. San Diego, California: Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-1030Lin, Bill Yuchen, Dong-Ho Lee, M. Shen, Ryan Rene Moreno, X. Huang, Prashant Shiralkar, and X. Ren. 2020. "TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8503-8511. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.752Lopez, C., Melissa Mekaoui, K. Aubry, Jean Bort, and Philippe Garnier. 2019. "Reconnaissance d'entités nommées itérative sur une structure en dépendances syntaxiques avec l'ontologie NERD." Revue des Nouvelles Technologies de l'Information, Extraction et Gestion des connaissances, RNTI-E-35, 81-92.Ma, Jie, Jun Liu, Y. Li, X. Hu, Yudai Pan, S. 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." 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In Proceedings of the Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), 338-343. https://doi.org/10.1109/SNAMS.2019.8931850Shang, Jingbo, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, and Jiawei Han. 2018. "Learning Named Entity Tagger using Domain-Specific Dictionary." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2054-2064. Brussels, Belgium: Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1230Shen, Jiaming, Ruiliang Lyu, Xiang Ren, Michelle Vanni, Brian Sadler, and Jiawei Han. 2019. "Mining entity synonyms with efficient neural set generation." In Proceedings of the AAAI Conference on Artificial Intelligence, 33:249-256. doi:10.1609/aaai.v33i01.3301249. https://doi.org/10.1609/aaai.v33i01.3301249Shinyama, Yusuke, Satoshi Sekine, and Kiyoshi Sudo. 2002. "Automatic Paraphrase Acquisition from News Articles." 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How can Paidiom improve the neural machine translation of idioms?

In this paper we present research results with Paidiom, a text-preprocessing algorithm designed for 1) converting discontinuous multiword expressions (MWEs) into their continuous forms and 2) translemmatising them, i.e., converting source-text MWEs into their target-text equivalents, in order to improve the performance of current neural machine translation (NMT) systems. To test its effectiveness, an experiment with the NMT systems of VIP, Google Translate and DeepL has been carried out in the ES>EN translation direction with Verb-Noun Idiomatic Constructions (VNICs) in Spanish. The performance of Paidiom was compared to both the one of our previous algorithm (gApp) and to the manual conversion (our gold standard). In this regard, the promising results yielded by this study, the first one analysing Paidiom’s performance, will shed some light on new avenues for enhancing MWE-aware NMT systems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Computational Lexicon and Representational Model for Arabic Multiword Expressions

The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations. This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions. This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena

Current trends

Deep parsing is the fundamental process aiming at the representation of the syntactic structure of phrases and sentences. In the traditional methodology this process is based on lexicons and grammars representing roughly properties of words and interactions of words and structures in sentences. Several linguistic frameworks, such as Headdriven Phrase Structure Grammar (HPSG), Lexical Functional Grammar (LFG), Tree Adjoining Grammar (TAG), Combinatory Categorial Grammar (CCG), etc., offer different structures and combining operations for building grammar rules. These already contain mechanisms for expressing properties of Multiword Expressions (MWE), which, however, need improvement in how they account for idiosyncrasies of MWEs on the one hand and their similarities to regular structures on the other hand. This collaborative book constitutes a survey on various attempts at representing and parsing MWEs in the context of linguistic theories and applications

Representation and parsing of multiword expressions

This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches