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

D6.1: Technologies and Tools for Lexical Acquisition

This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)

Extended papers from the MWE 2017 workshop

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

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

Syntaxe computationnelle du hongrois : de l'analyse en chunks à la sous-catégorisation verbale

We present the creation of two resources for Hungarian NLP applications: a rule-based shallow parser and a database of verbal subcategorization frames. Hungarian, as a non-configurational language with a rich morphology, presents specific challenges for NLP at the level of morphological and syntactic processing. While efficient and precise morphological analyzers are already available, Hungarian is under-resourced with respect to syntactic analysis. Our work aimed at overcoming this problem by providing resources for syntactic processing. Hungarian language is characterized by a rich morphology and a non-configurational encoding of grammatical functions. These features imply that the syntactic processing of Hungarian has to rely on morphological features rather than on constituent order. The broader interest of our undertaking is to propose representations and methods that are adapted to these specific characteristics, and at the same time are in line with state of the art research methodologies. More concretely, we attempt to adapt current results in argument realization and lexical semantics to the task of labeling sentence constituents according to their syntactic function and semantic role in Hungarian. Syntax and semantics are not completely independent modules in linguistic analysis and language processing: it has been known for decades that semantic properties of words affect their syntactic distribution. Within the syntax-semantics interface, the field of argument realization deals with the (partial or complete) prediction of verbal subcategorization from semantic properties. Research on verbal lexical semantics and semantically motivated mapping has been concentrating on predicting the syntactic realization of arguments, taking for granted (either explicitly or implicitly) that the distinction between arguments and adjuncts is known, and that adjuncts' syntactic realization is governed by productive syntactic rules, not lexical properties. However, besides the correlation between verbal aspect or actionsart and time adverbs (e.g. Vendler, 1967 or Kiefer, 1992 for Hungarian), the distribution of adjuncts among verbs or verb classes did not receive significant attention, especially within the lexical semantics framework. We claim that contrary to the widely shared presumption, adjuncts are often not fully productive. We therefore propose a gradual notion of productivity, defined in relation to Levin-type lexical semantic verb classes (Levin, 1993; Levin and Rappaport-Hovav, 2005). The definition we propose for the argument-adjunct dichotomy is based on evidence from Hungarian and exploits the idea that lexical semantics not only influences complement structure but is the key to the argument-adjunct distinction and the realization of adjunctsLa linguistique informatique est un domaine de recherche qui se concentre sur les méthodes et les perspectives de la modélisation formelle (statistique ou symbolique) de la langue naturelle. La linguistique informatique, tout comme la linguistique théorique, est une discipline fortement modulaire : les niveaux d'analyse linguistique comprennent la segmentation, l'analyse morphologique, la désambiguïsation, l'analyse syntaxique et sémantique. Tandis qu'un nombre d'outils existent déjà pour les traitements de bas niveau (analyse morphologique, étiquetage grammatical), le hongrois peut être considéré comme une langue peu doté pour l'analyse syntaxique et sémantique. Le travail décrit dans la présente thèse vise à combler ce manque en créant des ressources pour le traitement syntaxique du hongrois : notamment, un analyseur en chunks et une base de données lexicale de schémas de sous-catégorisation verbale. La première partie de la recherche présentée ici se concentre sur la création d'un analyseur syntaxique de surface (ou analyseur en chunks) pour le hongrois. La sortie de l'analyseur de surface est conçue pour servir d'entrée pour un traitement ultérieur visant à annoter les relations de dépendance entre le prédicat et ses compléments essentiels et circonstanciels. L'analyseur profond est mis en œuvre dans NooJ (Silberztein, 2004) en tant qu'une cascade de grammaires. Le deuxième objectif de recherche était de proposer une représentation lexicale pour la structure argumentale en hongrois. Cette représentation doit pouvoir gérer la vaste gamme de phénomènes qui échappent à la dichotomie traditionnelle entre un complément essentiel et un circonstanciel (p. ex. des structures partiellement productives, des écarts entre la prédictibilité syntaxique et sémantique). Nous avons eu recours à des résultats de la recherche récente sur la réalisation d'arguments et choisi un cadre qui répond à nos critères et qui est adaptable à une langue non-configurationnelle. Nous avons utilisé la classification sémantique de Levin (1993) comme modèle. Nous avons adapté les notions relatives à cette classification, à savoir celle de la composante sémantique et celle de l'alternance syntaxique, ainsi que la méthodologie d'explorer et de décrire le comportement des prédicats à l'aide de cette représentation, à la tâche de construire une représentation lexicale des verbes dans une langue non-configurationnelle. La première étape consistait à définir les règles de codage et de construire un vaste base de données lexicale pour les verbes et leurs compléments. Par la suite, nous avons entrepris deux expériences pour l'enrichissement de ce lexique avec des informations sémantiques lexicales afin de formaliser des généralisations syntaxiques et sémantiques pertinentes sur les classes de prédicats sous-jacentes. La première approche que nous avons testée consistait en une élaboration manuelle de classification de verbes en fonction de leur structure de compléments et de l'attribution de rôles sémantiques à ces compléments. Nous avons cherché la réponse aux questions suivantes: quelles sont les composants sémantiques pertinents pour définir une classification sémantique des prédicats hongrois? Quelles sont les implications syntaxiques spécifiques à ces classes? Et, plus généralement, quelle est la nature des alternances spécifiques aux classes verbales en hongrois ? Dans la phase finale de la recherche, nous avons étudié le potentiel de l'acquisition automatique pour extraire des classes de verbes à partir de corpus. Nous avons effectué une classification non supervisée, basée sur des données distributionnelles, pour obtenir une classification sémantique pertinente des verbes hongrois. Nous avons également testé la méthode de classification non supervisée sur des données françaises

Structurally informed methods for improved sentiment analysis

Sentiment analysis deals with methods to automatically analyze opinions in natural language texts, e.g., product reviews. Such reviews contain a large number of fine-grained opinions, but to automatically extract detailed information it is necessary to handle a wide variety of verbalizations of opinions. The goal of this thesis is to develop robust structurally informed models for sentiment analysis which address challenges that arise from structurally complex verbalizations of opinions. In this thesis, we look at two examples for such verbalizations that benefit from including structural information into the analysis: negation and comparisons. Negation directly influences the polarity of sentiment expressions, e.g., while "good" is positive, "not good" expresses a negative opinion. We propose a machine learning approach that uses information from dependency parse trees to determine whether a sentiment word is in the scope of a negation expression. Comparisons like "X is better than Y" are the main topic of this thesis. We present a machine learning system for the task of detecting the individual components of comparisons: the anchor or predicate of the comparison, the entities that are compared, which aspect they are compared in, and which entity is preferred. Again, we use structural context from a dependency parse tree to improve the performance of our system. We discuss two ways of addressing the issue of limited availability of training data for our system. First, we create a manually annotated corpus of comparisons in product reviews, the largest such resource available to date. Second, we use the semi-supervised method of structural alignment to expand a small seed set of labeled sentences with similar sentences from a large set of unlabeled sentences. Finally, we work on the task of producing a ranked list of products that complements the isolated prediction of ratings and supports the user in a process of decision making. We demonstrate how we can use the information from comparisons to rank products and evaluate the result against two conceptually different external gold standard rankings.Sentimentanalyse befasst sich mit Methoden zur automatischen Analyse von Meinungen in Texten wie z.B. Produktbewertungen. Solche bewertenden Texte enthalten detaillierte Meinungsäußerungen. Um diese automatisch analysieren zu können müssen wir mit strukturell komplexen Äußerungen umgehen können. In dieser Arbeit präsentieren wir einen Ansatz für die robuste Analyse von komplexen Meinungsäußerungen mit Hilfe von Informationen aus der Satzstruktur. Wir betrachten zwei Beispiele für komplexe Meinungsäußerungen: Negationen und Vergleiche. Eine Negation hat direkten Einfluss auf die Polarität einer Meinungsäußerung in einem Satz. Während "gut" eine positive Meinung ausdrückt, ist "nicht gut" negativ. Wir präsentieren ein System, das auf maschinellem Lernen beruht und Informationen aus dem Satzstrukturbaum verwendet um für ein gegebenes Schlüsselwort festzustellen, ob im Kontext eine Negation vorkommt die die Polarität beeinflusst. Als zweites Beispiel für komplexe Meinungsäußerungen betrachten wir Vergleiche von Produkten, z.B. "X ist besser als Y". Wir präsentieren ein lernendes System, das die einzelnen Komponenten von Vergleichen identifiziert: Das Prädikat bzw. das Wort, das den Vergleich einführt, die beiden Entitäten, die verglichen werden, der Aspekt in dem sie verglichen werden, und welche Entität als besser bewertet wird. Auch hier verwenden wir Satzstrukturinformationen um die Erkennung zu verbessern. Ein Problem für die Anwendung von maschinellen Lernverfahren ist die eingeschränkte Verfügbarkeit von Trainingsdaten. Wir gehen dieses Problem auf zwei Arten an. Zum einen durch die Annotation eines eigenen Datensatzes von Vergleichen in Kamerabewertungen. Zum anderen indem wir eine halbüberwachte Methode einsetzen um eine kleine Menge von manuell annotierten Sätzen durch ähnliche Sätze aus einer großen Menge unannotierter Sätze zu ergänzen. Abschließend bearbeiten wir die Aufgabe, den Auswahlprozess eines Kunden zu unterstützen indem wir eine Rangfolge von Produkten erstellen. Wir demonstrieren, wie wir Vergleiche zu diesem Zweck nutzen können und evaluieren unser System gegen zwei konzeptionell unterschiedliche Rangfolgen aus externen Quellen