This paper highlights the importance of integrating MWE identification with the development of syntactic MWE lexicons. It suggests that lexicons with minimal morphosyntactic information can amplify current MWE-annotated datasets and refine identification strategies. To our knowledge, this work represents the first attempt to focus on both seen and unseen of VMWEs for Arabic. It also deals with the challenge of differentiating between literal and figurative interpretations of idiomatic expressions. The approach involves a dual-phase procedure: first projecting a VMWE lexicon onto a corpus to identify candidate occurrences, then disambiguating these occurrences to distinguish idiomatic from literal instances. Experiments outlined in the paper aim to assess the efficacy of this technique, utilizing a lexicon known as LEXAR and the “parseme-ar” corpus. The findings suggest that lexicon-driven strategies have the potential to refine MWE identification, particularly for unseen occurrences.

This paper presents the objectives, organization and activities of the UniDive COST Action, a scientific network dedicated to universality, diversity and idiosyncrasy in language technology. We describe the objectives and organization of this initiative, the people involved, the working groups and the ongoing tasks and activities. This paper is also an pen call for participation towards new members and countries.

Multiword Expressions (MWEs) make a goodcase study for linguistic diversity due to theiridiosyncratic nature. Defining MWE canonicalforms as types, diversity may be measurednotably through disparity, based on pairwisedistances between types. To this aim, wetrain static MWE-aware word embeddings forverbal MWEs in 14 languages, and we showinteresting properties of these vector spaces.We use these vector spaces to implement theso-called functional diversity measure. Weapply this measure to the results of severalMWE identification systems. We find that,although MWE vector spaces are meaningful ata local scale, the disparity measure aggregatingthem at a global scale strongly correlateswith the number of types, which questions itsusefulness in presence of simpler diversitymetrics such as variety. We make the vectorspaces we generated available.

Multiword expressions (MWEs) pose difficulties for natural language processing (NLP) due to their linguistic features, such as syntactic and semantic properties, which distinguish them from regular word groupings. This paper describes a combination of two systems: one that learns verbal multiword expressions (VMWEs) and another that learns non-verbal MWEs (nVMWEs). Together, these systems leverage training data from both types of MWEs to enhance performance on a cross-type dataset containing both VMWEs and nVMWEs. Such scenarios emerge when datasets are developed using differing annotation schemes. We explore the fine-tuning of several state-of-the-art neural transformers for each MWE type. Our experiments demonstrate the advantages of the combined system over multi-task approaches or single-task models, addressing the challenges posed by diverse tagsets within the training data. Specifically, we evaluated the combined system on a French treebank named Sequoia, which features an annotation layer encompassing all syntactic types of French MWEs. With this combined approach, we improved the F1-score by approximately 3% on the Sequoia dataset.

We present version 1.3 of the PARSEME multilingual corpus annotated with verbal multiword expressions. Since the previous version, new languages have joined the undertaking of creating such a resource, some of the already existing corpora have been enriched with new annotated texts, while others have been enhanced in various ways. The PARSEME multilingual corpus represents 26 languages now. All monolingual corpora therein use Universal Dependencies v.2 tagset. They are (re-)split observing the PARSEME v.1.2 standard, which puts impact on unseen VMWEs. With the current iteration, the corpus release process has been detached from shared tasks; instead, a process for continuous improvement and systematic releases has been introduced.

Past research advocates that, in order to handle the unpredictable nature of multiword expressions (MWEs), their identification should be assisted with lexicons. The choice of the format for such lexicons, however, is far from obvious. We propose the first – to our knowledge – method to quantitatively evaluate some MWE lexicon formalisms based on the notion of observational adequacy. We apply it to derive a simple yet adequate MWE-lexicon formalism, dubbed λ-CSS, based on syntactic dependencies. It proves competitive with lexicons based on sequential representation of MWEs, and even comparable to a state-of-the art MWE identifier.

Multiword expressions (MWEs) are challenging and pervasive phenomena whose idiosyncratic properties show notably at the levels of lexicon, morphology, and syntax. Thus, they should best be annotated jointly with morphosyntax. We discuss two multilingual initiatives, Universal Dependencies and PARSEME, addressing these annotation layers in cross-lingually unified ways. We compare the annotation principles of these initiatives with respect to MWEs, and we put forward a roadmap towards their gradual unification. The expected outcomes are more consistent treebanking and higher universality in modeling idiosyncrasy.

This paper describes our efforts to extend the PARSEME framework to Modern Standard Arabic. Theapplicability of the PARSEME guidelines was tested by measuring the inter-annotator agreement in theearly annotation stage. A subset of 1,062 sentences from the Prague Arabic Dependency Treebank PADTwas selected and annotated by two Arabic native speakers independently. Following their annotations, anew Arabic corpus with over 1,250 annotated VMWEs has been built. This corpus already exceeds thesmallest corpora of the PARSEME suite, and enables first observations. We discuss our annotation guide-line schema that shows full MWE annotation is realizable in Arabic where we get good inter-annotator agreement.

Cet article décrit nos efforts pour étendre le projet PARSEME à l’arabe standard moderne. L’applicabilité du guide d’annotation de PARSEME a été testée en mesurant l’accord inter-annotateurs dès la première phase d’annotation. Un sous-ensemble de 1062 phrases du Prague Arabic Dependency Treebank (PADT) a été sélectionné et annoté indépendamment par deux locutrices natives arabes. Suite à leurs annotations, un nouveau corpus arabe avec plus de 1250 expressions polylexicales verbales (EPV) annotées a été construit.

The PARSEME (Parsing and Multiword Expressions) project proposes multilingual corpora annotated for multiword expressions (MWEs). In this case study, we focus on the Turkish corpus of PARSEME. Turkish is an agglutinative language and shows high inflection and derivation in word forms. This can cause some issues in terms of automatic morphosyntactic annotation. We provide an overview of the problems observed in the morphosyntactic annotation of the Turkish PARSEME corpus. These issues are mostly observed on the lemmas, which is important for the approximation of a type of an MWE. We propose modifications of the original corpus with some enhancements on the lemmas and parts of speech. The enhancements are then evaluated with an identification system from the PARSEME Shared Task 1.2 to detect MWEs, namely Seen2Seen. Results show increase in the F-measure for MWE identification, emphasizing the necessity of robust morphosyntactic annotation for MWE processing, especially for languages that show high surface variability.

Diversity can be decomposed into three distinct concepts, namely: variety, balance and disparity. This paper borrows from the extensive formalization and measures of diversity developed in ecology in order to evaluate the variety and balance of multiword expression annotation produced by automatic annotation systems. The measures of richness, normalized richness, and two variations of Hill’s evenness are considered in this paper. We observe how these measures behave against increasingly smaller samples of gold annotations of multiword expressions and use their comportment to validate or invalidate their pertinence for multiword expressions in annotated texts. We apply the validated measures to annotations in 14 languages produced by systems during the PARSEME shared task on automatic identification of multiword expressions and on the gold versions of the corpora. We also explore the limits of such evaluation by studying the impact of lemmatization errors in the Turkish corpus used in the shared task.

Automatic identification of multiword expressions (MWEs), like ‘to cut corners’ (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general.

Cet article présente le système développé par l’équipe DOING pour la campagne d’évaluation DEFT 2020 portant sur la similarité sémantique et l’extraction d’information fine. L’équipe a participé uniquement à la tâche 3 : “extraction d’information”. Nous avons utilisé une cascade de CRF pour annoter les différentes informations à repérer. Nous nous sommes concentrés sur la question de l’imbrication des entités et de la pertinence d’un type d’entité pour apprendre à reconnaître un autre. Nous avons également testé l’utilisation d’une ressource externe, MedDRA, pour améliorer les performances du système et d’un pipeline plus complexe mais ne gérant pas l’imbrication des entités. Nous avons soumis 3 runs et nous obtenons en moyenne sur toutes les classes des F-mesures de 0,64, 0,65 et 0,61.

This paper describes a manually annotated corpus of verbal multi-word expressions in Polish. It is among the 4 biggest datasets in release 1.2 of the PARSEME multiligual corpus. We describe the data sources, as well as the annotation process and its outcomes. We also present interesting phenomena encountered during the annotation task and put forward enhancements for the PARSEME annotation guidelines.

We present edition 1.2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs). Lessons learned from previous editions indicate that VMWEs have low ambiguity, and that the major challenge lies in identifying test instances never seen in the training data. Therefore, this edition focuses on unseen VMWEs. We have split annotated corpora so that the test corpora contain around 300 unseen VMWEs, and we provide non-annotated raw corpora to be used by complementary discovery methods. We released annotated and raw corpora in 14 languages, and this semi-supervised challenge attracted 7 teams who submitted 9 system results. This paper describes the effort of corpus creation, the task design, and the results obtained by the participating systems, especially their performance on unseen expressions.

We describe the Seen2Unseen system that participated in edition 1.2 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). The identification of VMWEs that do not appear in the provided training corpora (called unseen VMWEs) – with a focus here on verb-noun VMWEs – is based on mutual information and lexical substitution or translation of seen VMWEs. We present the architecture of the system, report results for 14 languages, and propose an error analysis.

Nous présentons le démonstrateur en-ligne du projet ANR PARSEME-FR dédié aux expressions polylexicales. Il inclut différents outils d’identification de telles expressions et un outil d’exploration des ressources linguistiques de ce projet.

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 minimal NLP-oriented lexicon, and we propose a roadmap for the MWE community driven by these requirements.

One of the most outstanding properties of multiword expressions (MWEs), especially verbal ones (VMWEs), important both in theoretical models and applications, is their idiosyncratic variability. Some MWEs are always continuous, while some others admit certain types of insertions. Components of some MWEs are rarely or never modified, while some others admit either specific or unrestricted modification. This unpredictable variability profile of MWEs hinders modeling and processing them as “words-with-spaces” on the one hand, and as regular syntactic structures on the other hand. Since variability of MWEs is a matter of scale rather than a binary property, we propose a 2-dimensional language-independent measure of variability dedicated to verbal MWEs based on syntactic and discontinuity-related clues. We assess its relevance with respect to a linguistic benchmark and its utility for the tasks of VMWE classification and variant identification on a French corpus.

Multiword expressions, especially verbal ones (VMWEs), show idiosyncratic variability, which is challenging for NLP applications, hence the need for VMWE identification. We focus on the task of variant identification, i.e. identifying variants of previously seen VMWEs, whatever their surface form. We model the problem as a classification task. Syntactic subtrees with previously seen combinations of lemmas are first extracted, and then classified on the basis of features relevant to morpho-syntactic variation of VMWEs. Feature values are both absolute, i.e. hold for a particular VMWE candidate, and relative, i.e. based on comparing a candidate with previously seen VMWEs. This approach outperforms a baseline by 4 percent points of F-measure on a French corpus.

This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year’s shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for most languages, corpora for some new languages, and new evaluation settings. Corpora were created for 20 languages, which are also briefly discussed. We report organizational principles behind the shared task and the evaluation metrics employed for ranking. The 17 participating systems, their methods and obtained results are also presented and analysed.

We describe the VarIDE system (standing for Variant IDEntification) which participated in the edition 1.1 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). Our system focuses on the task of VMWE variant identification by using morphosyntactic information in the training data to predict if candidates extracted from the test corpus could be idiomatic, thanks to a naive Bayes classifier. We report results for 19 languages.

Multiword expressions (MWEs) are linguistic objects containing two or more words and showing idiosyncratic behavior at different levels. Treebanks with annotated MWEs enable studies of such properties, as well as training and evaluation of MWE-aware parsers. However, few treebanks contain full-fledged MWE annotations. We show how this gap can be bridged in Polish by projecting 3 MWE resources on a constituency treebank.

Multiword expressions (MWEs) are known as a “pain in the neck” for NLP due to their idiosyncratic behaviour. While some categories of MWEs have been addressed by many studies, verbal MWEs (VMWEs), such as to take a decision, to break one’s heart or to turn off, have been rarely modelled. This is notably due to their syntactic variability, which hinders treating them as “words with spaces”. We describe an initiative meant to bring about substantial progress in understanding, modelling and processing VMWEs. It is a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for 18 languages. Its main outcome is a multilingual 5-million-word annotated corpus which underlies a shared task on automatic identification of VMWEs. This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems.

Nous décrivons la partie française des données produites dans le cadre de la campagne multilingue PARSEME sur l’identification d’expressions polylexicales verbales (Savary et al., 2017). Les expressions couvertes pour le français sont les expressions verbales idiomatiques, les verbes intrinsèquement pronominaux et une généralisation des constructions à verbe support. Ces phénomènes ont été annotés sur le corpus French-UD (Nivre et al., 2016) et le corpus Sequoia (Candito & Seddah, 2012), soit un corpus de 22 645 phrases, pour un total de 4 962 expressions annotées. On obtient un ratio d’une expression annotée tous les 100 tokens environ, avec un fort taux d’expressions discontinues (40%).

This paper describes a pilot study in lexical encoding of multi-word expressions (MWEs) in 4 Latin American dialects of Spanish: Costa Rican, Colombian, Mexican and Peruvian. We describe the variability of MWE usage across dialects. We adapt an existing data model to a dialect-aware encoding, so as to represent dialect-related specificities, while avoiding redundancy of the data common for all dialects. A dozen of linguistic properties of MWEs can be expressed in this model, both on the level of a whole MWE and of its individual components. We describe the resulting lexical resource containing several dozens of MWEs in four dialects and we propose a method for constructing a web corpus as a support for crowdsourcing examples of MWE occurrences. The resource is available under an open license and paves the way towards a large-scale dialect-aware language resource construction, which should prove useful in both traditional and novel NLP applications.

This paper summarizes the preliminary results of an ongoing survey on multiword resources carried out within the IC1207 Cost Action PARSEME (PARSing and Multi-word Expressions). Despite the availability of language resource catalogs and the inventory of multiword datasets on the SIGLEX-MWE website, multiword resources are scattered and difficult to find. In many cases, language resources such as corpora, treebanks, or lexical databases include multiwords as part of their data or take them into account in their annotations. However, these resources need to be centralized to make them accessible. The aim of this survey is to create a portal where researchers can easily find multiword(-aware) language resources for their research. We report on the design of the survey and analyze the data gathered so far. We also discuss the problems we have detected upon examination of the data as well as possible ways of enhancing the survey.

By means of an online survey, we have investigated ways in which various types of multiword expressions are annotated in existing treebanks. The results indicate that there is considerable variation in treatments across treebanks and thereby also, to some extent, across languages and across theoretical frameworks. The comparison is focused on the annotation of light verb constructions and verbal idioms. The survey shows that the light verb constructions either get special annotations as such, or are treated as ordinary verbs, while VP idioms are handled through different strategies. Based on insights from our investigation, we propose some general guidelines for annotating multiword expressions in treebanks. The recommendations address the following application-based needs: distinguishing MWEs from similar but compositional constructions; searching distinct types of MWEs in treebanks; awareness of literal and nonliteral meanings; and normalization of the MWE representation. The cross-lingually and cross-theoretically focused survey is intended as an aid to accessing treebanks and an aid for further work on treebank annotation.

This paper reports a critical analysis of the ISO TimeML standard, in the light of several experiences of temporal annotation that were conducted on spoken French. It shows that the norm suffers from weaknesses that should be corrected to fit a larger variety of needs inNLP and in corpus linguistics. We present our proposition of some improvements of the norm before it will be revised by the ISO Committee in 2017. These modifications concern mainly (1) Enrichments of well identified features of the norm: temporal function of TIMEX time expressions, additional types for TLINK temporal relations; (2) Deeper modifications concerning the units or features annotated: clarification between time and tense for EVENT units, coherence of representation between temporal signals (the SIGNAL unit) and TIMEX modifiers (the MOD feature); (3) A recommendation to perform temporal annotation on top of a syntactic (rather than lexical) layer (temporal annotation on a treebank).

Multiword expressions (MWEs) are pervasive in natural languages and often have both idiomatic and compositional readings, which leads to high syntactic ambiguity. We show that for some MWE types idiomatic readings are usually the correct ones. We propose a heuristic for an A* parser for Tree Adjoining Grammars which benefits from this knowledge by promoting MWE-oriented analyses. This strategy leads to a substantial reduction in the parsing search space in case of true positive MWE occurrences, while avoiding parsing failures in case of false positives.

This paper attempts a preliminary interpretation of the occurrence of different types of linguistic constructs in the manually-annotated Polish Coreference Corpus by providing analyses of various statistical properties related to mentions, clusters and near-identity links. Among others, frequency of mentions, zero subjects and singleton clusters is presented, as well as the average mention and cluster size. We also show that some coreference clustering constraints, such as gender or number agreement, are frequently not valid in case of Polish. The need for lemmatization for automatic coreference resolution is supported by an empirical study. Correlation between cluster and mention count within a text is investigated, with short characteristics of outlier cases. We also examine this correlation in each of the 14 text domains present in the corpus and show that none of them has abnormal frequency of outlier texts regarding the cluster/mention ratio. Finally, we report on our negative experiences concerning the annotation of the near-identity relation. In the conclusion we put forward some guidelines for the future research in the area.

Le projet EmotiRob, soutenu par l’agence nationale de la recherche, s’est donné pour objectif de détecter des émotions dans un contexte d’application original : la réalisation d’un robot compagnon émotionnel pour des enfants fragilisés. Nous présentons dans cet article le système qui caractérise l’émotion induite par le contenu linguistique des propos de l’enfant. Il se base sur un principe de compositionnalité des émotions, avec une valeur émotionnelle fixe attribuée aux mots lexicaux, tandis que les verbes et les adjectifs agissent comme des fonctions dont le résultat dépend de la valeur émotionnelle de leurs arguments. L’article présente la méthode de calcul utilisée, ainsi que la norme lexicale émotionnelle correspondante. Une analyse quantitative et qualitative des premières expérimentations présente les différences entre les sorties du module de détection et l’annotation d’experts, montrant des résultats satisfaisants, avec la bonne détection de la valence émotionnelle dans plus de 90% des cas.

We present the named entity annotation task within the on-going project of the National Corpus of Polish. To the best of our knowledge, this is the first attempt at a large-scale corpus annotation of Polish named entities. We describe the scope and the TEI-inspired hierarchy of named entities admitted for this task, as well as the TEI-conformant multi-level stand-off annotation format. We also discuss some methodological strategies including the annotation of embedded, coordinated and discontinuous names. Our annotation platform consists of two main tools interconnected by converting facilities. A rule-based natural language processing platform SProUT is used for the automatic pre-annotation of named entities, due to the previously created Polish extraction grammars adapted to the annotation task. A customizable graphical tree editor TrEd, extended to our needs, provides an ergonomic environment for manual correction of annotations. Despite some difficult cases encountered in the early annotation phase, about 2,600 named entities in 1,800 corpus sentences have presently been annotated, which allowed to validate the project methodology and tools.

Le projet ANR Emotirob aborde la question de la détection des émotions sous un cadre original : concevoir un robot compagnon émotionnel pour enfants fragilisés. Notre approche consiste à combiner détection linguistique et prosodie. Nos expériences montrent qu’un sujet humain peut estimer de manière fiable la valence émotionnelle d’un énoncé à partir de son contenu propositionnel. Nous avons donc développé un premier modèle de détection linguistique qui repose sur le principe de compositionnalité des émotions : les mots simples ont une valence émotionnelle donnée et les prédicats modifient la valence de leurs arguments. Après une description succincte du système logique de compréhension dont les sorties sont utilisées pour le calcul global de l’émotion, cet article présente la construction d’une norme émotionnelle lexicale de référence, ainsi que d’une ontologie de classes émotionnelles de prédicats, pour des enfants de 5 et 7 ans.