Automatic Identification of Aspectual Classes across Verbal Readings

International audienceThe automatic prediction of aspectual classes is very challenging for verbs whose aspectual value varies across readings, which are the rule rather than the exception. This paper sheds a new perspective on this problem by using a machine learning approach and a rich morpho-syntactic and semantic valency lexicon.In contrast to previous work, where the aspectual value of corpus clauses is determined on the basis of features retrieved from the corpus, we use features extracted from the lexicon, and aim to predict the aspectual value of verbal \textit{readings} rather than verbs.Studying the performance of the classifiers on a set of manually annotated verbal readings, we found that our lexicon provided enough information to reliably predict the aspectual value of verbs across their readings.We additionally tested our predictions for unseen predicates through a task based evaluation, by using them in the automatic detection of temporal relation types in TempEval 2007 tasks for French. These experiments also confirmed the reliability of our aspectual predictions, even for unseen verbs

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

A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches

Aspectual meaning refers to how the internal temporal structure of situations is presented. This includes whether a situation is described as a state or as an event, whether the situation is finished or ongoing, and whether it is viewed as a whole or with a focus on a particular phase. This survey gives an overview of computational approaches to modeling lexical and grammatical aspect along with intuitive explanations of the necessary linguistic concepts and terminology. In particular, we describe the concepts of stativity, telicity, habituality, perfective and imperfective, as well as influential inventories of eventuality and situation types. We argue that because aspect is a crucial component of semantics, especially when it comes to reporting the temporal structure of situations in a precise way, future NLP approaches need to be able to handle and evaluate it systematically in order to achieve human-level language understanding.Comment: Accepted at EACL 2023, camera ready versio

Automatic prediction of aspectual class of verbs in context

This paper describes a new approach to predicting the aspectual class of verbs in context, i.e., whether a verb is used in a stative or dynamic sense. We identify two challenging cases of this problem: when the verb is unseen in training data, and when the verb is ambiguous for aspectual class. A semi-supervised approach using linguistically-motivated features and a novel set of distributional features based on representative verb types allows us to predict classes accurately, even for unseen verbs. Many frequent verbs can be either stative or dynamic in different contexts, which has not been modeled by previous work; we use contextual features to resolve this ambiguity. In addition, we introduce two new datasets of clauses marked for aspectual class

Situation entity annotation

This paper presents an annotation scheme for a new semantic annotation task with relevance for analysis and computation at both the clause level and the discourse level. More specifically, we label the finite clauses of texts with the type of situation entity (e.g., eventualities, statements about kinds, or statements of belief) they introduce to the discourse, following and extending work by Smith (2003). We take a feature-driven approach to annotation, with the result that each clause is also annotated with fundamental aspectual class, whether the main NP referent is specific or generic, and whether the situation evoked is episodic or habitual. This annotation is performed (so far) on three sections of the MASC corpus, with each clause labeled by at least two annotators. In this paper we present the annotation scheme, statistics of the corpus in its current version, and analyses of both inter-annotator agreement and intra-annotator consistency

Processing temporal information in unstructured documents

Tese de doutoramento, Informática (Ciência da Computação), Universidade de Lisboa, Faculdade de Ciências, 2013Temporal information processing has received substantial attention in the last few years, due to the appearance of evaluation challenges focused on the extraction of temporal information from texts written in natural language. This research area belongs to the broader field of information extraction, which aims to automatically find specific pieces of information in texts, producing structured representations of that information, which can then be easily used by other computer applications. It has the potential to be useful in several applications that deal with natural language, given that many languages, among which we find Portuguese, extensively refer to time. Despite that, temporal processing is still incipient for many language, Portuguese being one of them. The present dissertation has various goals. On one hand, it addresses this current gap, by developing and making available resources that support the development of tools for this task, employing this language, and also by developing precisely this kind of tools. On the other hand, its purpose is also to report on important results of the research on this area of temporal processing. This work shows how temporal processing requires and benefits from modeling different kinds of knowledge: grammatical knowledge, logical knowledge, knowledge about the world, etc. Additionally, both machine learning methods and rule-based approaches are explored and used in the development of hybrid systems that are capable of taking advantage of the strengths of each of these two types of approach.O processamento de informação temporal tem recebido bastante atenção nos últimos anos, devido ao surgimento de desafios de avaliação focados na extração de informação temporal de textos escritos em linguagem natural. Esta área de investigação enquadra-se no campo mais lato da extração de informação, que visa encontrar automaticamente informação específica presente em textos, produzindo representações estruturadas da mesma, que podem depois ser facilmente utilizadas por outras aplicações computacionais. Tem o potencial de ser útil em diversas aplicações que lidam com linguagem natural, dado o caráter quase ubíquo da referência ao tempo cronólogico em muitas línguas, entre as quais o Português. Apesar de tudo, o processamento temporal encontra-se ainda incipiente para bastantes línguas, sendo o Português uma delas. A presente dissertação tem vários objetivos. Por um lado vem colmatar esta lacuna existente, desenvolvendo e disponibilizando recursos que suportam o desenvolvimento de ferramentas para esta tarefa, utilizando esta língua, e desenvolvendo também precisamente este tipo de ferramentas. Por outro serve também para relatar resultados importantes da pesquisa nesta área do processamento temporal. Neste trabalho, mostra- -se como o processamento temporal requer e beneficia da modelação de conhecimento de diversos níveis: gramatical, lógico, acerca do mundo, etc. Adicionalmente, são explorados tanto métodos de aprendizagem automática como abordagens baseadas em regras, desenvolvendo-se sistemas híbridos capazes de tirar partido das vantagens de cada um destes dois tipos de abordagem.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/40140/2007

Classification of telicity using cross-linguistic annotation projection

This paper addresses the automatic recognition of telicity, an aspectual notion. A telic event includes a natural endpoint (“she walked home”), while an atelic event does not (“she walked around”). Recognizing this difference is a prerequisite for temporal natural language understanding. In English, this classification task is difficult, as telicity is a covert linguistic category. In contrast, in Slavic languages, aspect is part of a verb’s meaning and even available in machine-readable dictionaries. Our contributions are as follows. We successfully leverage additional silver standard training data in the form of projected annotations from parallel English-Czech data as well as context information, improving automatic telicity classification for English significantly compared to previous work. We also create a new data set of English texts manually annotated with telicity

Situation entity types: automatic classification of clause-level aspect

This paper describes the first robust approach to automatically labeling clauses with their situation entity type (Smith, 2003), capturing aspectual phenomena at the clause level which are relevant for interpreting both semantics at the clause level and discourse structure. Previous work on this task used a small data set from a limited domain, and relied mainly on words as features, an approach which is impractical in larger settings. We provide a new corpus of texts from 13 genres (40,000 clauses) annotated with situation entity types. We show that our sequence labeling approach using distributional information in the form of Brown clusters, as well as syntactic-semantic features targeted to the task, is robust across genres, reaching accuracies of up to 76%

The present perfect : a corpus-based investigation

On the basis of an investigation of a corpus of 5.5 million words, this thesis analyses the use of the present perfect in modem American and British English. The investigation traces the development of the present perfect from its origins as a structure with adjectival meaning to its modern-day use as an aspectual verb form. A frequency analysis tests the claims of various writers that the present perfect is losing ground against the preterite and is less frequent in American than in British English. Neither claim is supported by the results of this analysis. A temporal specifier analysis investigates the co-occurrence of a large number of adverbials with the various verb forms. It finds that certain groups of specifiers which have hitherto been considered markers for the present perfect are in fact very poor indicators. Specifiers indicating a period of time lasting up to the moment of utterance, however, are found to be very reliable indicators. With one exception no significant difference was found between the British and American corpora in this respect. A functional-semantic analysis examines the various theories of the present perfect against the background of the results of the empirical investigation and finds them to be insufficient in one or more respects. In the final chapter the division between tense and aspect is shown to be artificial and a model of the present perfect is presented which is based on the idea of multilayered aspectual values. The model is centred on the unifying concept of phragmatisation - the closing of the event time-frame. According to this model, discourse topics involving the present perfect are perceived to describe an event which takes place in a time frame which is not closed to the deictic zero point at the moment of utterance. The final section describes which factors are operative in the phragmatisation or closing of event time frames