Marc Moens

Named Entity recognition without gazetteers

It is often claimed that Named Entity recognition systems need extensive gazetteers---lists of names of people, organisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named Entity recognition systems.We report on a Named Entity recognition system which combines rule-based grammars with statistical (maximum entropy) models. We report on the systems performance with gazetteers of different types and different sizes, using test material from the MUC-7 competition. We show that, for the text type and task of this competition, it is sufficient to use relatively small gazetteers of well-known names, rather than large gazetteers of low-frequency names. We conclude with observations about the domain independence of the competition and of our experiments.

Improvements in Automatic Thesaurus Extraction

The use of semantic resources is common in modern NLP systems, but methods to extract lexical semantics have only recently begun to perform well enough for practical use. We evaluate existing and new similarity metrics for thesaurus extraction, and experiment with the trade-off between extraction performance and efficiency. We propose an approximation algorithm, based on canonical attributes and coarse- and fine-grained matching, that reduces the time complexity and execution time of thesaurus extraction with only a marginal performance penalty.

An annotation scheme for discourse-level argumentation in research articles

In order to build robust automatic abstracting systems, there is a need for better training resources than are currently available. In this paper, we introduce an annotation scheme for scientific articles which can be used to build such a resource in a consistent way. The seven categories of the scheme are based on rhetorical moves of argumentation. Our experimental results show that the scheme is stable, reproducible and intuitive to use.

TEMPORAL ONTOLOGY IN NATURAL LANGUAGE

A semantics of linguistic categories like tense, aspect, and certain temporal adverbials, and a theory of their use in defining the temporal relations of events, both require a more complex structure on the domain underlying the meaning representations than is commonly assumed. The paper proposes an ontology based on such notions as causation and consequence, rather than on purely temporal primitives. We claim that any manageable logic or other formal system for natural language temporal descriptions will have to embody such an ontology, as will any usable temporal database for knowledge about events which is to be interrogated using natural language.

Scaling Context Space

Context is used in many NLP systems as an indicator of a terms syntactic and semantic function. The accuracy of the system is dependent on the quality and quantity of contextual information available to describe each term. However, the quantity variable is no longer fixed by limited corpus resources. Given fixed training time and computational resources, it makes sense for systems to invest time in extracting high quality contextual information from a fixed corpus. However, with an effectively limitless quantity of text available, extraction rate and representation size need to be considered. We use thesaurus extraction with a range of context extracting tools to demonstrate the interaction between context quantity, time and size on a corpus of 300 million words.

Algorithms for analysing the temporal structure of discourse

We describe a method for analysing the temporal structure of a discourse which takes into account the effects of tense, aspect, temporal adverbials and rhetorical structure and which minimises unnecessary ambiguity in the temporal structure. It is part of a discourse grammar implemented in Carpenters ALE formalism. The method for building up the temporal structure of the discourse combines constraints and prefernces: we use constraints to reduce the number of possible structures, exploiting the HPSG type hierarchy and unification for this purpose; and we apply preferences to choose between the remaining options using a temporal centering mechanism. We end by recommending that an underspecified representation of the structure using these techniques be used to avoid generating the temporal/rhetorical structure until higher-level information can be used to disambiguate.

What's Yours and What's Mine: Determining Intellectual Attribution in Scientific Text

We believe that identifying the structure of scientific argumentation in articles can help in tasks such as automatic summarization or the automated construction of citation indexes. One particularly important aspect of this structure is the question of who a given scientific statement is attributed to: other researchers, the field in general, or the authors themselves.We present the algorithm and a systematic evaluation of a system which can recognize the most salient textual properties that contribute to the global argumentative structure of a text. In this paper we concentrate on two particular features, namely the occurrences of prototypical agents and their actions in scientific text.

PRIORITY UNION AND GENERALIZATION IN DISCOURSE GRAMMARS

We describe an implementation in Carpenters typed feature formalism, ALE, of a discourse grammar of the kind proposed by Scha, Polanyi, et al. We examine their method for resolving parallelism-dependent anaphora and show that there is a coherent feature-structural rendition of this type of grammar which uses the operations of priority union and generalization. We describe an augmentation of the ALE system to encompass these operations and we show that an appropriate choice of definition for priority union gives the desired multiple output for examples of VP-ellipsis which exhibit a strict/sloppy ambiguity.

Expressing generalizations in unification-based grammar formalisms

This paper shows how higher levels of generalization can be introduced into unification grammars by exploiting methods for typing grammatical objects. We discuss the strategy of using global declarations to limit possible linguistic structures, and sketch a few unusual aspects of our type-checking algorithm. We also describe the sort system we use in our semantic representation language and illustrate the expressive power gained by being able to state global constraints over these sorts. Finally, we briefly illustrate the sort system by applying it to some agreement phenomena and to problems of adjunct resolution.

The Dynamic Interpretation of Natural Language

The Basic Research Action DYANA (“Dynamic Interpretation of Natural Language”) is concerned with foundational research towards the development of an integrated computational model of language interpretation, covering the spectrum from speech to reasoning. The programme of work focuses on the following themes in natural language understanding: