Alex Lascarides

Temporal interpretation, discourse relations and commonsense entailment

This paper presents a formal account of how to determine the discourse relations between propositions introduced in a text, and the relations between the events they describe. The distinct natural interpretations of texts with similar syntax are explained in terms of defeasible rules. These characterise the effects of causal knowledge and knowledge of language use on interpretation. Patterns of defeasible entailment that are supported by the logic in which the theory is expressed are shown to underly temporal interpretation.

An Algebra for Semantic Construction in Constraint-based Grammars

We develop a framework for formalizing semantic construction within grammars expressed in typed feature structure logics, including HPSG. The approach provides an alternative to the lambda calculus; it maintains much of the desirable flexibility of unification-based approaches to composition, while constraining the allowable operations in order to capture basic generalizations and improve maintainability.

Using automatically labelled examples to classify rhetorical relations: An assessment

Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very time-consuming to create. However, rhetorical relations are sometimes lexically marked, i.e., signalled by discourse markers (e.g., because, but, consequently etc.), and it has been suggested (Marcu and Echihabi, 2002) that the presence of these cues in some examples can be exploited to label them automatically with the corresponding relation. The discourse markers are then removed and the automatically labelled data are used to train a classifier to determine relations even when no discourse marker is present (based on other linguistic cues such as word co-occurrences). In this paper, we investigate empirically how feasible this approach is. In particular, we test whether automatically labelled, lexically marked examples are really suitable training material for classifiers that are then applied to unmarked examples. Our results suggest that training on this type of data may not be such a good strategy, as models trained in this way do not seem to generalise very well to unmarked data. Furthermore, we found some evidence that this behaviour is largely independent of the classifiers used and seems to lie in the data itself (e.g., marked and unmarked examples may be too dissimilar linguistically and removing unambiguous markers in the automatic labelling process may lead to a meaning shift in the examples).

A Statistical Approach to the Semantics of Verb-Particles

This paper describes a distributional approach to the semantics of verb-particle constructions (e.g. put up, make off). We report first on a framework for implementing and evaluating such models. We then go on to report on the implementation of some techniques for using statistical models acquired from corpus data to infer the meaning of verb-particle constructions.

The Semantics and Pragmatics of Presupposition

In this paper, we offer a novel analysis of presuppositions, paying particular attention to the interaction between the knowledge resources that are required to interpret them. The analysis has two main features. First, we capture an analogy between presuppositions, anaphora and scope ambiguity (cf. van der Sandt I992), by utilizing semantic underspecification (cf. Reyle I993). Second, resolving this underspecification requires reasoning about how the presupposition is rhetorically connected to the discourse context. This has several consequences. First, since pragmatic information plays a role in computing the rhetorical relation, it also constrains the interpretation of presuppositions. Our account therefore provides a formal framework for analysing problematic data, which require pragmatic reasoning. Second, binding presuppositions to the context via rhetorical links replaces accommodating them, in the sense of adding them to the context (cf. Lewis I979). The treatment of presupposition is thus generalized and integrated into the discourse update procedure. We formalize this approach in SDRT (Asher I993; Lascarides & Asher I993), and demonstrate that it provides a rich framework for interpreting presuppositions, where semantic and pragmatic constraints are integrated

Pragmatics and word meaning

In this paper, we explore the interaction between lexical semantics and pragmatics. We argue that linguistic processing is informationally encapsulated and utilizes relatively simple ‘taxonomic’ lexical semantic knowledge. On this basis, defeasible lexical generalisations deliver defeasible parts of logical form. In contrast, pragmatic inference is open-ended and involves arbitrary real-world knowledge. Two axioms specify when pragmatic defaults override lexical ones. We demonstrate that modelling this interaction allows us to achieve a more refined interpretation of words in a discourse context than either the lexicon or pragmatics could do on their own.

Questions in Dialogue

In this paper we explore how compositional semantics, discourse structure, and the cognitive states of participants all contribute to pragmatic constraints on answers to questions in dialogue. We synthesise formal semantic theories on questions and answers with techniques for discourse interpretation familiar from computational linguistics, and show how this provides richer constraints on responses in dialogue than either component can achieve alone.

Learning sentence-internal temporal relations

In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like after, which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporal marker achieve good performance on a pseudo-disambiguation task simulating temporal inference (during testing the temporal marker is treated as unseen and the models must select the right marker from a set of possible candidates). Secondly, we assess whether the proposed approach holds promise for the semi-automatic creation of temporal annotations. Specifically, we use a model trained on noisy and approximate data (i.e., main and subordinate clauses) to predict intra-sentential relations present in TimeBank, a corpus annotated rich temporal information. Our experiments compare and contrast several probabilistic models differing in their feature space, linguistic assumptions and data requirements. We evaluate performance against gold standard corpora and also against human subjects.

Abducing Temporal Discourse

We focus on the following question: given the causal and temporal relations between events in a knowledge base, what are the ways they can be described in extended text? We argue that we want to be able to generate laconic text, where certain temporal information remains implicit but pragmatically inferrable. An algorithm for generating laconic text is proposed, interleaving abduction and nonmonotonic deduction over a formal model of pragmatic implicature. We demonstrate that the nonmonotonicity ensures that the generation of laconic text is influenced by the preceding linguistic and extra-linguistic context.

Inferring discourse relations in context

We investigate various contextual effects on text interpretation, and account for them by providing contextual constraints in a logical theory of text interpretation. On the basis of the way these constraints interact with the other knowledge sources, we draw some general conclusions about the role of domain-specific information, top-down and bottom-up discourse information flow, and the usefulness of formalisation in discourse theory.