Carlos Ramisch


2020

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Multilingual enrichment of disease biomedical ontologies
Léo Bouscarrat | Antoine Bonnefoy | Cécile Capponi | Carlos Ramisch
Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)

Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both, plus Arabic, Chinese and Russian for the second. We first use direct links between Wikidata and the studied ontologies and then use second-order links by going through other intermediate ontologies. We then compare the quality of the translations obtained thanks to Wikidata with a commercial machine translation tool, here Google Cloud Translation.

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Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons
Stella Markantonatou | John McCrae | Jelena Mitrović | Carole Tiberius | Carlos Ramisch | Ashwini Vaidya | Petya Osenova | Agata Savary
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

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Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expressions
Carlos Ramisch | Agata Savary | Bruno Guillaume | Jakub Waszczuk | Marie Candito | Ashwini Vaidya | Verginica Barbu Mititelu | Archna Bhatia | Uxoa Iñurrieta | Voula Giouli | Tunga Gungor | Menghan Jiang | Timm Lichte | Chaya Liebeskind | Johanna Monti | Renata Ramisch | Sara Stymne | Abigail Walsh | Hongzhi Xu
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

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.

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Seen2Unseen at PARSEME Shared Task 2020: All Roads do not Lead to Unseen Verb-Noun VMWEs
Caroline Pasquer | Agata Savary | Carlos Ramisch | Jean-Yves Antoine
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

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.

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Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?
Caroline Pasquer | Agata Savary | Carlos Ramisch | Jean-Yves Antoine
Proceedings of the 28th International Conference on Computational Linguistics

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.

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SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings
Cindy Aloui | Carlos Ramisch | Alexis Nasr | Lucie Barque
Proceedings of the 28th International Conference on Computational Linguistics

Contextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances. However, their opaqueness makes it hard to interpret their behaviour. SLICE is a hybrid model that combines supersense labels with contextual embeddings. We introduce a weakly supervised method to learn interpretable embeddings from raw corpora and small lists of seed words. Our model is able to represent both a word and its context as embeddings into the same compact space, whose dimensions correspond to interpretable supersenses. We assess the model in a task of supersense tagging for French nouns. The little amount of supervision required makes it particularly well suited for low-resourced scenarios. Thanks to its interpretability, we perform linguistic analyses about the predicted supersenses in terms of input word and context representations.

2019

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Without lexicons, multiword expression identification will never fly: A position statement
Agata Savary | Silvio Cordeiro | Carlos Ramisch
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

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.

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The Impact of Word Representations on Sequential Neural MWE Identification
Nicolas Zampieri | Carlos Ramisch | Geraldine Damnati
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

Recent initiatives such as the PARSEME shared task 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 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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Unsupervised Compositionality Prediction of Nominal Compounds
Silvio Cordeiro | Aline Villavicencio | Marco Idiart | Carlos Ramisch
Computational Linguistics, Volume 45, Issue 1 - March 2019

Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.

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Typological Features for Multilingual Delexicalised Dependency Parsing
Manon Scholivet | Franck Dary | Alexis Nasr | Benoit Favre | Carlos Ramisch
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)

The existence of universal models to describe the syntax of languages has been debated for decades. The availability of resources such as the Universal Dependencies treebanks and the World Atlas of Language Structures make it possible to study the plausibility of universal grammar from the perspective of dependency parsing. Our work investigates the use of high-level language descriptions in the form of typological features for multilingual dependency parsing. Our experiments on multilingual parsing for 40 languages show that typological information can indeed guide parsers to share information between similar languages beyond simple language identification.

2018

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Towards a Variability Measure for Multiword Expressions
Caroline Pasquer | Agata Savary | Jean-Yves Antoine | Carlos Ramisch
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

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.

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If you’ve seen some, you’ve seen them all: Identifying variants of multiword expressions
Caroline Pasquer | Agata Savary | Carlos Ramisch | Jean-Yves Antoine
Proceedings of the 27th International Conference on Computational Linguistics

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.

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Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
Agata Savary | Carlos Ramisch | Jena D. Hwang | Nathan Schneider | Melanie Andresen | Sameer Pradhan | Miriam R. L. Petruck
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

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Edition 1.1 of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions
Carlos Ramisch | Silvio Ricardo Cordeiro | Agata Savary | Veronika Vincze | Verginica Barbu Mititelu | Archna Bhatia | Maja Buljan | Marie Candito | Polona Gantar | Voula Giouli | Tunga Güngör | Abdelati Hawwari | Uxoa Iñurrieta | Jolanta Kovalevskaitė | Simon Krek | Timm Lichte | Chaya Liebeskind | Johanna Monti | Carla Parra Escartín | Behrang QasemiZadeh | Renata Ramisch | Nathan Schneider | Ivelina Stoyanova | Ashwini Vaidya | Abigail Walsh
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

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.

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VarIDE at PARSEME Shared Task 2018: Are Variants Really as Alike as Two Peas in a Pod?
Caroline Pasquer | Carlos Ramisch | Agata Savary | Jean-Yves Antoine
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

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.

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Veyn at PARSEME Shared Task 2018: Recurrent Neural Networks for VMWE Identification
Nicolas Zampieri | Manon Scholivet | Carlos Ramisch | Benoit Favre
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

This paper describes the Veyn system, submitted to the closed track of the PARSEME Shared Task 2018 on automatic identification of verbal multiword expressions (VMWEs). Veyn is based on a sequence tagger using recurrent neural networks. We represent VMWEs using a variant of the begin-inside-outside encoding scheme combined with the VMWE category tag. In addition to the system description, we present development experiments to determine the best tagging scheme. Veyn is freely available, covers 19 languages, and was ranked ninth (MWE-based) and eight (Token-based) among 13 submissions, considering macro-averaged F1 across languages.

2017

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Annotation d’expressions polylexicales verbales en français (Annotation of verbal multiword expressions in French)
Marie Candito | Mathieu Constant | Carlos Ramisch | Agata Savary | Yannick Parmentier | Caroline Pasquer | Jean-Yves Antoine
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

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%).

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Survey: Multiword Expression Processing: A Survey
Mathieu Constant | Gülşen Eryiǧit | Johanna Monti | Lonneke van der Plas | Carlos Ramisch | Michael Rosner | Amalia Todirascu
Computational Linguistics, Volume 43, Issue 4 - December 2017

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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Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
Stella Markantonatou | Carlos Ramisch | Agata Savary | Veronika Vincze
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

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The PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions
Agata Savary | Carlos Ramisch | Silvio Cordeiro | Federico Sangati | Veronika Vincze | Behrang QasemiZadeh | Marie Candito | Fabienne Cap | Voula Giouli | Ivelina Stoyanova | Antoine Doucet
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

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.

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Discovering Light Verb Constructions and their Translations from Parallel Corpora without Word Alignment
Natalie Vargas | Carlos Ramisch | Helena Caseli
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

We propose a method for joint unsupervised discovery of multiword expressions (MWEs) and their translations from parallel corpora. First, we apply independent monolingual MWE extraction in source and target languages simultaneously. Then, we calculate translation probability, association score and distributional similarity of co-occurring pairs. Finally, we rank all translations of a given MWE using a linear combination of these features. Preliminary experiments on light verb constructions show promising results.

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Identification of Ambiguous Multiword Expressions Using Sequence Models and Lexical Resources
Manon Scholivet | Carlos Ramisch
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

We present a simple and efficient tagger capable of identifying highly ambiguous multiword expressions (MWEs) in French texts. It is based on conditional random fields (CRF), using local context information as features. We show that this approach can obtain results that, in some cases, approach more sophisticated parser-based MWE identification methods without requiring syntactic trees from a treebank. Moreover, we study how well the CRF can take into account external information coming from a lexicon.

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LexSubNC: A Dataset of Lexical Substitution for Nominal Compounds
Rodrigo Wilkens | Leonardo Zilio | Silvio Ricardo Cordeiro | Felipe Paula | Carlos Ramisch | Marco Idiart | Aline Villavicencio
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

2016

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mwetoolkit+sem: Integrating Word Embeddings in the mwetoolkit for Semantic MWE Processing
Silvio Cordeiro | Carlos Ramisch | Aline Villavicencio
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents mwetoolkit+sem: an extension of the mwetoolkit that estimates semantic compositionality scores for multiword expressions (MWEs) based on word embeddings. First, we describe our implementation of vector-space operations working on distributional vectors. The compositionality score is based on the cosine distance between the MWE vector and the composition of the vectors of its member words. Our generic system can handle several types of word embeddings and MWE lists, and may combine individual word representations using several composition techniques. We evaluate our implementation on a dataset of 1042 English noun compounds, comparing different configurations of the underlying word embeddings and word-composition models. We show that our vector-based scores model non-compositionality better than standard association measures such as log-likelihood.

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DeQue: A Lexicon of Complex Prepositions and Conjunctions in French
Carlos Ramisch | Alexis Nasr | André Valli | José Deulofeu
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We introduce DeQue, a lexicon covering French complex prepositions (CPRE) like “à partir de” (from) and complex conjunctions (CCONJ) like “bien que” (although). The lexicon includes fine-grained linguistic description based on empirical evidence. We describe the general characteristics of CPRE and CCONJ in French, with special focus on syntactic ambiguity. Then, we list the selection criteria used to build the lexicon and the corpus-based methodology employed to collect entries. Finally, we quantify the ambiguity of each construction by annotating around 100 sentences randomly taken from the FRWaC. In addition to its theoretical value, the resource has many potential practical applications. We intend to employ DeQue for treebank annotation and to train a dependency parser that can takes complex constructions into account.

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Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time
Silvio Cordeiro | Carlos Ramisch | Marco Idiart | Aline Villavicencio
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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How Naked is the Naked Truth? A Multilingual Lexicon of Nominal Compound Compositionality
Carlos Ramisch | Silvio Cordeiro | Leonardo Zilio | Marco Idiart | Aline Villavicencio
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Filtering and Measuring the Intrinsic Quality of Human Compositionality Judgments
Carlos Ramisch | Silvio Cordeiro | Aline Villavicencio
Proceedings of the 12th Workshop on Multiword Expressions

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UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense Tagging
Silvio Cordeiro | Carlos Ramisch | Aline Villavicencio
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Never-Ending Multiword Expressions Learning
Alexandre Rondon | Helena Caseli | Carlos Ramisch
Proceedings of the 11th Workshop on Multiword Expressions

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Joint Dependency Parsing and Multiword Expression Tokenization
Alexis Nasr | Carlos Ramisch | José Deulofeu | André Valli
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Nothing like Good Old Frequency: Studying Context Filters for Distributional Thesauri
Muntsa Padró | Marco Idiart | Aline Villavicencio | Carlos Ramisch
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Comparing the Quality of Focused Crawlers and of the Translation Resources Obtained from them
Bruno Laranjeira | Viviane Moreira | Aline Villavicencio | Carlos Ramisch | Maria José Finatto
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Comparable corpora have been used as an alternative for parallel corpora as resources for computational tasks that involve domain-specific natural language processing. One way to gather documents related to a specific topic of interest is to traverse a portion of the web graph in a targeted way, using focused crawling algorithms. In this paper, we compare several focused crawling algorithms using them to collect comparable corpora on a specific domain. Then, we compare the evaluation of the focused crawling algorithms to the performance of linguistic processes executed after training with the corresponding generated corpora. Also, we propose a novel approach for focused crawling, exploiting the expressive power of multiword expressions.

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Comparing Similarity Measures for Distributional Thesauri
Muntsa Padró | Marco Idiart | Aline Villavicencio | Carlos Ramisch
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Distributional thesauri have been applied for a variety of tasks involving semantic relatedness. In this paper, we investigate the impact of three parameters: similarity measures, frequency thresholds and association scores. We focus on the robustness and stability of the resulting thesauri, measuring inter-thesaurus agreement when testing different parameter values. The results obtained show that low-frequency thresholds affect thesaurus quality more than similarity measures, with more agreement found for increasing thresholds.These results indicate the sensitivity of distributional thesauri to frequency. Nonetheless, the observed differences do not transpose over extrinsic evaluation using TOEFL-like questions. While this may be specific to the task, we argue that a careful examination of the stability of distributional resources prior to application is needed.

2013

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Proceedings of the 9th Workshop on Multiword Expressions
Valia Kordoni | Carlos Ramisch | Aline Villavicencio
Proceedings of the 9th Workshop on Multiword Expressions

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Identifying Pronominal Verbs: Towards Automatic Disambiguation of the Clitic ‘se’ in Portuguese
Magali Sanches Duran | Carolina Evaristo Scarton | Sandra Maria Aluísio | Carlos Ramisch
Proceedings of the 9th Workshop on Multiword Expressions

2012

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Une plate-forme générique et ouverte pour le traitement des expressions polylexicales (An Open and Generic Framework for the Acquisition of Multiword Expressions) [in French]
Carlos Ramisch
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 3: RECITAL

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Get out but don’t fall down: verb-particle constructions in child language
Aline Villavicencio | Marco Idiart | Carlos Ramisch | Vítor Araújo | Beracah Yankama | Robert Berwick
Proceedings of the Workshop on Computational Models of Language Acquisition and Loss

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A Broad Evaluation of Techniques for Automatic Acquisition of Multiword Expressions
Carlos Ramisch | Vitor De Araujo | Aline Villavicencio
Proceedings of ACL 2012 Student Research Workshop

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A Generic Framework for Multiword Expressions Treatment: from Acquisition to Applications
Carlos Ramisch
Proceedings of ACL 2012 Student Research Workshop

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A Comparable Corpus Based on Aligned Multilingual Ontologies
Roger Granada | Lucelene Lopes | Carlos Ramisch | Cassia Trojahn | Renata Vieira | Aline Villavicencio
Proceedings of the First Workshop on Multilingual Modeling

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A Serious Game for Building a Portuguese Lexical-Semantic Network
Mathieu Mangeot | Carlos Ramisch
Proceedings of the 3rd Workshop on the People’s Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP

2011

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Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
Valia Kordoni | Carlos Ramisch | Aline Villavicencio
Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World

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Identifying and Analyzing Brazilian Portuguese Complex Predicates
Magali Sanches Duran | Carlos Ramisch | Sandra Maria Aluísio | Aline Villavicencio
Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World

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Fast and Flexible MWE Candidate Generation with the mwetoolkit
Vitor De Araujo | Carlos Ramisch | Aline Villavicencio
Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World

2010

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Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications
Éric Laporte | Preslav Nakov | Carlos Ramisch | Aline Villavicencio
Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications

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Web-based and combined language models: a case study on noun compound identification
Carlos Ramisch | Aline Villavicencio | Christian Boitet
Coling 2010: Posters

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Multiword Expressions in the wild? The mwetoolkit comes in handy
Carlos Ramisch | Aline Villavicencio | Christian Boitet
Coling 2010: Demonstrations

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mwetoolkit: a Framework for Multiword Expression Identification
Carlos Ramisch | Aline Villavicencio | Christian Boitet
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper presents the Multiword Expression Toolkit (mwetoolkit), an environment for type and language-independent MWE identification from corpora. The mwetoolkit provides a targeted list of MWE candidates, extracted and filtered according to a number of user-defined criteria and a set of standard statistical association measures. For generating corpus counts, the toolkit provides both a corpus indexation facility and a tool for integration with web search engines, while for evaluation, it provides validation and annotation facilities. The mwetoolkit also allows easy integration with a machine learning tool for the creation and application of supervised MWE extraction models if annotated data is available. In our experiment, the mwetoolkit was tested and evaluated in the context of MWE extraction in the biomedical domain. Our preliminary results show that the toolkit performs better than other approaches, especially concerning recall. Moreover, this first version can also be extended in several ways in order to improve the quality of the results.

2008

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Picking them up and Figuring them out: Verb-Particle Constructions, Noise and Idiomaticity
Carlos Ramisch | Aline Villavicencio | Leonardo Moura | Marco Idiart
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

2007

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Validation and Evaluation of Automatically Acquired Multiword Expressions for Grammar Engineering
Aline Villavicencio | Valia Kordoni | Yi Zhang | Marco Idiart | Carlos Ramisch
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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