Étiquetage multilingue en parties du discours avec MElt

International audienceWe describe recent evolutions of MElt, a discriminative part-of-speech tagging system. MElt is targeted at the optimal exploitation of information provided by external lexicons for improving its performance over models trained solely on annotated corpora. We have trained MElt on more than 40 datasets covering over 30 languages. Compared with the state-of-the-art system MarMoT, MElt's results are slightly worse on average when no external lexicon is used, but slightly better when such resources are available, resulting in state-of-the-art taggers for a number of languages.Nous présentons des travaux récents réalisés autour de MElt, système discriminant d'étiquetage en parties du discours. MElt met l'accent sur l'exploitation optimale d'informations lexicales externes pour améliorer les performances des étiqueteurs par rapport aux modèles entraînés seulement sur des corpus annotés. Nous avons entraîné MElt sur plus d'une quarantaine de jeux de données couvrant plus d'une trentaine de langues. Comparé au système état-de-l'art MarMoT, MElt obtient en moyenne des résultats légèrement moins bons en l'absence de lexique externe, mais meilleurs lorsque de telles ressources sont disponibles, produisant ainsi des étiqueteurs état-de-l'art pour plusieurs langues

Les disfluences dans les mots composés

National audienceLes disfluences, phénomène propre à l‟oral, ont la particularité de briser la linéarité syntaxique de l‟énoncé. Les mots composés ont tendance à former des unités syntaxiques et sémantiques. Dans cet article, nous montrons que l‟énonciation de telles expressions dans un discours oral est moins propice à l‟apparition de disfluences qu‟une séquence libre de mots. Pour cela, nous avons mis au point une procédure automatique de reconnaissance probabiliste des mots composés incluant une détection itérative préalable des disfluences

Representation and Processing of Composition, Variation and Approximation in Language Resources and Tools

In my habilitation dissertation, meant to validate my capacity of and maturity for directingresearch activities, I present a panorama of several topics in computational linguistics, linguisticsand computer science.Over the past decade, I was notably concerned with the phenomena of compositionalityand variability of linguistic objects. I illustrate the advantages of a compositional approachto the language in the domain of emotion detection and I explain how some linguistic objects,most prominently multi-word expressions, defy the compositionality principles. I demonstratethat the complex properties of MWEs, notably variability, are partially regular and partiallyidiosyncratic. This fact places the MWEs on the frontiers between different levels of linguisticprocessing, such as lexicon and syntax.I show the highly heterogeneous nature of MWEs by citing their two existing taxonomies.After an extensive state-of-the art study of MWE description and processing, I summarizeMultiflex, a formalism and a tool for lexical high-quality morphosyntactic description of MWUs.It uses a graph-based approach in which the inflection of a MWU is expressed in function ofthe morphology of its components, and of morphosyntactic transformation patterns. Due tounification the inflection paradigms are represented compactly. Orthographic, inflectional andsyntactic variants are treated within the same framework. The proposal is multilingual: it hasbeen tested on six European languages of three different origins (Germanic, Romance and Slavic),I believe that many others can also be successfully covered. Multiflex proves interoperable. Itadapts to different morphological language models, token boundary definitions, and underlyingmodules for the morphology of single words. It has been applied to the creation and enrichmentof linguistic resources, as well as to morphosyntactic analysis and generation. It can be integratedinto other NLP applications requiring the conflation of different surface realizations of the sameconcept.Another chapter of my activity concerns named entities, most of which are particular types ofMWEs. Their rich semantic load turned them into a hot topic in the NLP community, which isdocumented in my state-of-the art survey. I present the main assumptions, processes and resultsissued from large annotation tasks at two levels (for named entities and for coreference), parts ofthe National Corpus of Polish construction. I have also contributed to the development of bothrule-based and probabilistic named entity recognition tools, and to an automated enrichment ofProlexbase, a large multilingual database of proper names, from open sources.With respect to multi-word expressions, named entities and coreference mentions, I pay aspecial attention to nested structures. This problem sheds new light on the treatment of complexlinguistic units in NLP. When these units start being modeled as trees (or, more generally, asacyclic graphs) rather than as flat sequences of tokens, long-distance dependencies, discontinu-ities, overlapping and other frequent linguistic properties become easier to represent. This callsfor more complex processing methods which control larger contexts than what usually happensin sequential processing. Thus, both named entity recognition and coreference resolution comesvery close to parsing, and named entities or mentions with their nested structures are analogous3to multi-word expressions with embedded complements.My parallel activity concerns finite-state methods for natural language and XML processing.My main contribution in this field, co-authored with 2 colleagues, is the first full-fledged methodfor tree-to-language correction, and more precisely for correcting XML documents with respectto a DTD. We have also produced interesting results in incremental finite-state algorithmics,particularly relevant to data evolution contexts such as dynamic vocabularies or user updates.Multilingualism is the leitmotif of my research. I have applied my methods to several naturallanguages, most importantly to Polish, Serbian, English and French. I have been among theinitiators of a highly multilingual European scientific network dedicated to parsing and multi-word expressions. I have used multilingual linguistic data in experimental studies. I believethat it is particularly worthwhile to design NLP solutions taking declension-rich (e.g. Slavic)languages into account, since this leads to more universal solutions, at least as far as nominalconstructions (MWUs, NEs, mentions) are concerned. For instance, when Multiflex had beendeveloped with Polish in mind it could be applied as such to French, English, Serbian and Greek.Also, a French-Serbian collaboration led to substantial modifications in morphological modelingin Prolexbase in its early development stages. This allowed for its later application to Polishwith very few adaptations of the existing model. Other researchers also stress the advantages ofNLP studies on highly inflected languages since their morphology encodes much more syntacticinformation than is the case e.g. in English.In this dissertation I am also supposed to demonstrate my ability of playing an active rolein shaping the scientific landscape, on a local, national and international scale. I describemy: (i) various scientific collaborations and supervision activities, (ii) roles in over 10 regional,national and international projects, (iii) responsibilities in collective bodies such as program andorganizing committees of conferences and workshops, PhD juries, and the National UniversityCouncil (CNU), (iv) activity as an evaluator and a reviewer of European collaborative projects.The issues addressed in this dissertation open interesting scientific perspectives, in whicha special impact is put on links among various domains and communities. These perspectivesinclude: (i) integrating fine-grained language data into the linked open data, (ii) deep parsingof multi-word expressions, (iii) modeling multi-word expression identification in a treebank as atree-to-language correction problem, and (iv) a taxonomy and an experimental benchmark fortree-to-language correction approaches

Unsupervised Aspect Discovery from Online Consumer Reviews

The success of on-line review websites has led to an overwhelming number of on-line consumer reviews. These reviews have become an important tool for consumers when making a decision to purchase a product. This growth has led to the need for applications that enable this information to be presented in a way that is meaningful. These applications often rely on domain specific semantic lexicons which are both expensive and time consuming to make. The following thesis proposes an unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a two step hierarchical clustering process in which we first cluster based on the semantic similarity of the contexts of terms and then on the similarity of the hypernyms of the cluster members. The method also includes a process for assigning class labels to each of the clusters. Finally an experiment showing how the proposed methods can be used to measure aspect based sentiment is performed. The methods proposed in this thesis are evaluated on a set of 157,865 reviews from a major commercial website and found that the two-step clustering process increases cluster F-scores over a single round of clustering. Finally, the proposed methods are compared to a state of the art topic modelling approach by Titov and McDonald (2008)

Development and Design of Deep Learning-based Parts-of-Speech Tagging System for Azerbaijani language

Parts-of-Speech (POS) tagging, also referred to as word-class disambiguation, is one of the prerequisite techniques that are used as part of the advanced pre-processing stage across pipeline at the majority of natural language processing (NLP) applications. By using this tool as a preliminary step, most NLP software, such as Chat Bots, Translating Engines, Voice Recognitions, etc., assigns a prior part of speech to each word in the given data in order to identify or distinguish the grammatical category, so they can easily decipher the meaning of the word. This thesis addresses the novel approach to the issue related to the clarification of word context for the Azerbaijani language by using a deep learning-based automatic speech tagger on a clean (manually annotated) dataset. Azerbaijani is a member of the Turkish family and an agglutinative language. In contrast to other languages, recent research studies of speech taggers for the Azerbaijani language were unable to deliver efficient state of the art accuracy. Thus, in this thesis, study is being conducted to investigate how deep learning strategies such as simple recurrent neural networks (RNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) might be used to enhance the POS tagging capabilities of the Azerbaijani language

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

TALC-sef, Un corpus étiqueté de traductions littéraires en serbe, anglais et français

International audienceLe corpus TALC-sef (TAgged Literary Corpus in Serbian, English, French) est un corpus parallèle d'ouvrages littéraires en serbe, anglais et français, étiquetés en parties du discours et librement consultables via une interface en ligne. Il a été constitué par l'Université d'Arras, en collaboration avec l'Université Lille 3 et l'Université de Belgrade, dans une perspective d'études comparées en stylistique et linguistique. Le corpus TALC-sef représente au total plus de 2 millions de mots, il intègre notamment un corpus étiqueté, corrigé manuellement pour la langue serbe, de 150 000 mots. Dans cet article, nous présentons le mode de constitution du corpus parallèle dans son ensemble, puis nous nous attachons plus spécifiquement à l'élaboration du sous-corpus serbe étiqueté. Nous détaillons les choix linguistiques et techniques sous-jacents à la constitution de ce sous-corpus, qui vient compléter l'offre existante pour la linguistique sur corpus en serbe: à ce jour, le seul corpus librement disponible consiste en une traduction du roman 1984 de G. Orwell (100 000 mots), alors que nous proposons un corpus d'œuvres écrites à l'origine en Serbe, de 150 000 mots. La constitution de ce sous-corpus a permis l'élaboration de modèles d'étiquetage automatique pour trois étiqueteurs syntaxiques, dont Treetagger, TnT et BTagger, le plus efficace d'entre eux. Enfin, nous présentons les perspectives d'évolution du corpus existant, en termes d'enrichissement des annotations syntaxiques (analyses en dépendance en parallèle sur les trois langues), ainsi que les apports d'un tel corpus parallèle étiqueté pour la linguistique du français

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years