A methodology for the semiautomatic annotation of EPEC-RolSem, a basque corpus labeled at predicative level following the PropBank-Verb Net model

In this article we describe the methodology developed for the semiautomatic annotation of EPEC-RolSem, a Basque corpus labeled at predicate level following the PropBank-VerbNet model. The methodology presented is the product of detailed theoretical study of the semantic nature of verbs in Basque and of their similarities and differences with verbs in other languages. As part of the proposed methodology, we are creating a Basque lexicon on the PropBank-VerbNet model that we have named the Basque Verb Index (BVI). Our work thus dovetails the general trend toward building lexicons from tagged corpora that is clear in work conducted for other languages. EPEC-RolSem and BVI are two important resources for the computational semantic processing of Basque; as far as the authors are aware, they are also the first resources of their kind developed for Basque. In addition, each entry in BVI is linked to the corresponding verb-entry in well-known resources like PropBank, VerbNet, WordNet, Levin’s Classification and FrameNet. We have also implemented several automatic processes to aid in creating and annotating the BVI, including processes designed to facilitate the task of manual annotation.Lan honetan, EPEC-RolSem corpusa etiketatzeko jarraitu dugun metodologia deskribatuko dugu. EPEC-RolSem corpusa PropBank-VerbNet ereduari jarraiki predikatu-mailan etiketatutako euskarazko corpusa da. Etiketatze-lana aurrera eramateko euskal aditzen izaera semantikoa aztertu eta ingeleseko aditzekin konparatu dugu, azterketa horren emaitza da lan honetan proposatzen dugun metodologia. Metodologiaren atal bat PropBank-VerbNet eredura sortutako euskal aditzen lexikoiaren osaketa izan da, lexikoi hau Basque Verb Index (BVI) deitu dugu. Gure lanak alor honetan beste hizkuntzetan dagoen joera nagusia jarraitzen du, hau da, etiketatutako corpusetatik lexikoiak sortzea. EPEC-RolSem eta BVI oso baliabide garrantzitsuak dira euskararen semantika konputazionalaren alorrean, izan ere, euskararako sortutako mota honetako lehen baliabideak dira. Honetaz guztiaz gain, BVIko sarrera bakoitza PropBank, VerbNet, WordNet, Levinen sailkapena eta FrameNet bezalako baliabide ezagunekin lotua dago. Hainbat prozesu automatiko inplementatu ditugu EPEC-RolSem corpusaren eskuzko etiketatzea laguntzeko eta baita BVI sortzeko eta osatzeko ere

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

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. © 2017 The Author(s)</p

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

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

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

Italian VerbNet: A Construction-based Approach to Italian Verb Classification

L'elaborato consiste nella proposta di una nuova classificazione verbale per l'italiano, sulla base dell'autorevole modello inglese di VerbNet. Il metodo elaborato, punto centrale della ricerca, è stato sviluppato in modo da consentire la creazione di classi compatibili con il modello inglese, ma allo stesso tempo autonome e basate su criteri teorici indipendenti. Ad una parte esplicativa segue l'esposizione dei dati correlati da commenti

Formal Linguistic Models and Knowledge Processing. A Structuralist Approach to Rule-Based Ontology Learning and Population

2013 - 2014The main aim of this research is to propose a structuralist approach for knowledge processing by means of ontology learning and population, achieved starting from unstructured and structured texts. The method suggested includes distributional semantic approaches and NL formalization theories, in order to develop a framework, which relies upon deep linguistic analysis... [edited by author]XIII n.s