Identifying Participation of Individual Verbs or VerbNet Classes in the Causative Alternation

Verbs that participate in diathesis alternations have different semantics in their different syntactic environments, which need to be distinguished in order to process these verbs and their contexts correctly. We design and implement 8 approaches to the automatic identification of the causative alternation in English (3 based on VerbNet classes, 5 based on individual verbs). For verbs in this alternation, the semantic roles that contribute to the meaning of the verb can be associated with different syntactic slots. Our most successful approaches use distributional vectors and achieve an F1 score of up to 79% on a balanced test set. We also apply our approaches to the distinction between the causative alternation and the unexpressed object alternation. Our best system for this is based on syntactic information, with an F1 score of 75% on a balanced test set

Unsupervised Induction of Modern Standard Arabic Verb Classes

We exploit the resources in the Arabic Treebank (ATB) for the novel task of automatically creating lexical semantic verb classes for Modern Standard Arabic (MSA). Verbs are clustered into groups that share semantic elements of meaning as they exhibit similar syntactic behavior. The results of the clustering experiments are compared with a gold standard set of classes, which is approximated by using the noisy English translations provided in the ATB to create Levin-like classes for MSA. The quality of the clusters is found to be sensitive to the inclusion of information about lexical heads of the constituents in the syntactic frames, as well as parameters of the clustering algorithm. The best set of parameters yields an Fβ=1 score of 0.501, compared to a random baseline with an Fβ=1 score of 0.37

Verb similarity: comparing corpus and psycholinguistic data

Similarity, which plays a key role in fields like cognitive science, psycholinguistics and natural language processing, is a broad and multifaceted concept. In this work we analyse how two approaches that belong to different perspectives, the corpus view and the psycholinguistic view, articulate similarity between verb senses in Spanish. Specifically, we compare the similarity between verb senses based on their argument structure, which is captured through semantic roles, with their similarity defined by word associations. We address the question of whether verb argument structure, which reflects the expression of the events, and word associations, which are related to the speakers' organization of the mental lexicon, shape similarity between verbs in a congruent manner, a topic which has not been explored previously. While we find significant correlations between verb sense similarities obtained from these two approaches, our findings also highlight some discrepancies between them and the importance of the degree of abstraction of the corpus annotation and psycholinguistic representations.La similitud, que desempeña un papel clave en campos como la ciencia cognitiva, la psicolingüística y el procesamiento del lenguaje natural, es un concepto amplio y multifacético. En este trabajo analizamos cómo dos enfoques que pertenecen a diferentes perspectivas, la visión del corpus y la visión psicolingüística, articulan la semejanza entre los sentidos verbales en español. Específicamente, comparamos la similitud entre los sentidos verbales basados en su estructura argumental, que se capta a través de roles semánticos, con su similitud definida por las asociaciones de palabras. Abordamos la cuestión de si la estructura del argumento verbal, que refleja la expresión de los acontecimientos, y las asociaciones de palabras, que están relacionadas con la organización de los hablantes del léxico mental, forman similitud entre los verbos de una manera congruente, un tema que no ha sido explorado previamente. Mientras que encontramos correlaciones significativas entre las similitudes de los sentidos verbales obtenidas de estos dos enfoques, nuestros hallazgos también resaltan algunas discrepancias entre ellos y la importancia del grado de abstracción de la anotación del corpus y las representaciones psicolingüísticas.La similitud, que exerceix un paper clau en camps com la ciència cognitiva, la psicolingüística i el processament del llenguatge natural, és un concepte ampli i multifacètic. En aquest treball analitzem com dos enfocaments que pertanyen a diferents perspectives, la visió del corpus i la visió psicolingüística, articulen la semblança entre els sentits verbals en espanyol. Específicament, comparem la similitud entre els sentits verbals basats en la seva estructura argumental, que es capta a través de rols semàntics, amb la seva similitud definida per les associacions de paraules. Abordem la qüestió de si l'estructura de l'argument verbal, que reflecteix l'expressió dels esdeveniments, i les associacions de paraules, que estan relacionades amb l'organització dels parlants del lèxic mental, formen similitud entre els verbs d'una manera congruent, un tema que no ha estat explorat prèviament. Mentre que trobem correlacions significatives entre les similituds dels sentits verbals obtingudes d'aquests dos enfocaments, les nostres troballes també ressalten algunes discrepàncies entre ells i la importància del grau d'abstracció de l'anotació del corpus i les representacions psicolingüístiques

Investigating the cross-lingual translatability of VerbNet-style classification.

VerbNet-the most extensive online verb lexicon currently available for English-has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper

An investigation into deviant morphology : issues in the implementation of a deep grammar for Indonesian

This thesis investigates deviant morphology in Indonesian for the implementation of a deep grammar. In particular we focus on the implementation of the verbal suffix -kan. This suffix has been described as having many functions, which alter the kinds of arguments and the number of arguments the verb takes (Dardjowidjojo 1971; Chung 1976; Arka 1993; Vamarasi 1999; Kroeger 2007; Son and Cole 2008). Deep grammars or precision grammars (Butt et al. 1999a; Butt et al. 2003; Bender et al. 2011) have been shown to be useful for natural language processing (NLP) tasks, such as machine translation and generation (Oepen et al. 2004; Cahill and Riester 2009; Graham 2011), and information extraction (MacKinlay et al. 2012), demonstrating the need for linguistically rich information to aid NLP tasks. Although these linguistically-motivated grammars are invaluable resources to the NLP community, the biggest drawback is the time required for the manual creation and curation of the lexicon. Our work aims to expedite this process by applying methods to assign syntactic information to kan-affixed verbs automatically. The method we employ exploits the hypothesis that semantic similarity is tightly connected with syntactic behaviour (Levin 1993). Our endeavour in automatically acquiring verbal information for an Indonesian deep grammar poses a number of lingustic challenges. First of all Indonesian verbs exhibit voice marking that is characteristic of the subgrouping of its language family. In order to be able to characterise verbal behaviour in Indonesian, we first need to devise a detailed analysis of voice for implementation. Another challenge we face is the claim that all open class words in Indonesian, at least as it is spoken in some varieties (Gil 1994; Gil 2010), cannot linguistically be analysed as being distinct from each other. That is, there is no distiction between nouns, verbs or adjectives in Indonesian, and all word from the open class categories should be analysed uniformly. This poses difficulties in implementing a grammar in a linguistically motivated way, as well discovering syntactic behaviour of verbs, if verbs cannot be distinguished from nouns. As part of our investigation we conduct experiments to verify the need to employ word class categories, and we find that indeed these are linguistically motivated labels in Indonesian. Through our investigation into deviant morphological behaviour, we gain a better characterisation of the morphosyntactic effects of -kan, and we discover that, although Indonesian has been labelled as a language with no open word class distinctions, word classes can be established as being linguistically-motivated

Boom and Whoosh: Verbs of Explosion as a Change-of-State Class

Much research has been done on various semantic verb classes, most notably on break-verbs. In this study, a new class of change-of-state verbs is proposed, namely verbs that encode an explode-event. The research presented here not only offers a new organization of certain change-of-state verbs, but also highlights the issues that are ever-present in the classification of verbs. Eight verbs are investigated as possible members of this class: blast, blow up, burst, erupt, explode, detonate, go supernova, and pop. Using data from three corpora and survey results from 20 participants, this study explores the various verb alternations and constructions in which these explode-verbs participate across three distinct senses: change-of-state, appearance, and sound emission. In addition, in this study I look at the types of arguments that these verbs take, on semantic and syntactic levels. I conclude with the disqualification of go supernova as a possible member, due to strong syntactic dissimilarities with the other seven verbs. Overall, explode-verbs are shown to be unique enough to warrant the establishment of a separate sub-class under the Change-of-State macro-class

Acquiring and Harnessing Verb Knowledge for Multilingual Natural Language Processing

Advances in representation learning have enabled natural language processing models to derive non-negligible linguistic information directly from text corpora in an unsupervised fashion. However, this signal is underused in downstream tasks, where they tend to fall back on superficial cues and heuristics to solve the problem at hand. Further progress relies on identifying and filling the gaps in linguistic knowledge captured in their parameters. The objective of this thesis is to address these challenges focusing on the issues of resource scarcity, interpretability, and lexical knowledge injection, with an emphasis on the category of verbs. To this end, I propose a novel paradigm for efficient acquisition of lexical knowledge leveraging native speakers’ intuitions about verb meaning to support development and downstream performance of NLP models across languages. First, I investigate the potential of acquiring semantic verb classes from non-experts through manual clustering. This subsequently informs the development of a two-phase semantic dataset creation methodology, which combines semantic clustering with fine-grained semantic similarity judgments collected through spatial arrangements of lexical stimuli. The method is tested on English and then applied to a typologically diverse sample of languages to produce the first large-scale multilingual verb dataset of this kind. I demonstrate its utility as a diagnostic tool by carrying out a comprehensive evaluation of state-of-the-art NLP models, probing representation quality across languages and domains of verb meaning, and shedding light on their deficiencies. Subsequently, I directly address these shortcomings by injecting lexical knowledge into large pretrained language models. I demonstrate that external manually curated information about verbs’ lexical properties can support data-driven models in tasks where accurate verb processing is key. Moreover, I examine the potential of extending these benefits from resource-rich to resource-poor languages through translation-based transfer. The results emphasise the usefulness of human-generated lexical knowledge in supporting NLP models and suggest that time-efficient construction of lexicons similar to those developed in this work, especially in under-resourced languages, can play an important role in boosting their linguistic capacity.ESRC Doctoral Fellowship [ES/J500033/1], ERC Consolidator Grant LEXICAL [648909

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)

A distributional investigation of German verbs

Diese Dissertation bietet eine empirische Untersuchung deutscher Verben auf der Grundlage statistischer Beschreibungen, die aus einem großen deutschen Textkorpus gewonnen wurden. In einem kurzen Überblick über linguistische Theorien zur lexikalischen Semantik von Verben skizziere ich die Idee, dass die Verbbedeutung wesentlich von seiner Argumentstruktur (der Anzahl und Art der Argumente, die zusammen mit dem Verb auftreten) und seiner Aspektstruktur (Eigenschaften, die den zeitlichen Ablauf des vom Verb denotierten Ereignisses bestimmen) abhängt. Anschließend erstelle ich statistische Beschreibungen von Verben, die auf diesen beiden unterschiedlichen Bedeutungsfacetten basieren. Insbesondere untersuche ich verbale Subkategorisierung, Selektionspräferenzen und Aspekt. Alle diese Modellierungsstrategien werden anhand einer gemeinsamen Aufgabe, der Verbklassifikation, bewertet. Ich zeige, dass im Rahmen von maschinellem Lernen erworbene Merkmale, die verbale lexikalische Aspekte erfassen, für eine Anwendung von Vorteil sind, die Argumentstrukturen betrifft, nämlich semantische Rollenkennzeichnung. Darüber hinaus zeige ich, dass Merkmale, die die verbale Argumentstruktur erfassen, bei der Aufgabe, ein Verb nach seiner Aspektklasse zu klassifizieren, gut funktionieren. Diese Ergebnisse bestätigen, dass diese beiden Facetten der Verbbedeutung auf grundsätzliche Weise zusammenhängen.This dissertation provides an empirical investigation of German verbs conducted on the basis of statistical descriptions acquired from a large corpus of German text. In a brief overview of the linguistic theory pertaining to the lexical semantics of verbs, I outline the idea that verb meaning is composed of argument structure (the number and types of arguments that co-occur with a verb) and aspectual structure (properties describing the temporal progression of an event referenced by the verb). I then produce statistical descriptions of verbs according to these two distinct facets of meaning: In particular, I examine verbal subcategorisation, selectional preferences, and aspectual type. All three of these modelling strategies are evaluated on a common task, automatic verb classification. I demonstrate that automatically acquired features capturing verbal lexical aspect are beneficial for an application that concerns argument structure, namely semantic role labelling. Furthermore, I demonstrate that features capturing verbal argument structure perform well on the task of classifying a verb for its aspectual type. These findings suggest that these two facets of verb meaning are related in an underlying way