An automated tool to assist in the understanding of legacy code components can be useful both in the areas of software reuse and software maintenance. Most previous work in this area has concentrated on functionally-oriented code. Whereas object-oriented code has been shown to be inherently more reusable than functionally-oriented code, in many cases the eventual reuse of the object-oriented code was not considered during development. A knowledge-based, natural language processing approach to the automated understanding of object-oriented code as an aid to the reuse of object-oriented code is described. A system, called the PATRicia system (Program Analysis Tool for Reuse) that implements the approach is examined. The natural language processing/information extraction system that comprises a large part of the PATRicia system is discussed and the knowledge-base of the PATRicia system, in the form of conceptual graphs, is described. Reports provided by natural language-generation in the PATRicia system are described.

Resnik and Yarowsky (1997) made a set of observations about the state-of-the-art in automatic word sense disambiguation and, motivated by those observations, offered several specific proposals regarding improved evaluation criteria, common training and testing resources, and the definition of sense inventories. Subsequent discussion of those proposals resulted in SENSEVAL, the first evaluation exercise for word sense disambiguation (Kilgarriff and Palmer 2000). This article is a revised and extended version of our 1997 workshop paper, reviewing its observations and proposals and discussing them in light of the SENSEVAL exercise. It also includes a new in-depth empirical study of translingually-based sense inventories and distance measures, using statistics collected from native-speaker annotations of 222 polysemous contexts across 12 languages. These data show that monolingual sense distinctions at most levels of granularity can be effectively captured by translations into some set of second languages, especially as language family distance increases. In addition, the probability that a given sense pair will tend to lexicalize differently across languages is shown to correlate with semantic salience and sense granularity; sense hierarchies automatically generated from such distance matrices yield results remarkably similar to those created by professional monolingual lexicographers.

We show how the DOP model can be used for fast and robust context-sensitive processing of spoken input in a practical spoken dialogue system called OVIS. OVIS (Openbaar Vervoer Informatie Systeem) – ‘Public Transport Information System’, is a Dutch spoken language information system which operates over ordinary telephone lines. The prototype system is the immediate goal of the NWO Priority Programme ‘Language and Speech Technology’. In this paper, we extend the original Data-Oriented Parsing (DOP) model to context-sensitive interpretation of spoken input. The system we describe uses the OVIS corpus (which consists of 10,000 trees enriched with compositional semantics) to compute from an input word-graph the best utterance together with its meaning. Dialogue context is taken into account by dividing up the OVIS corpus into context-dependent subcorpora. Each system question triggers a subcorpus by which the user answer is analysed and interpreted. Our experiments indicate that the context-sensitive DOP model obtains better accuracy than the original model, allowing for fast and robust processing of spoken input.

This paper discusses the nature, history and current characteristics of Language Engineering, which is contrasted with Natural Language Processing and Computational Linguistics, and which is shown to have attained its own distinct identity in recent years. Major trends in the field are examined, including its focus on large-scale practical tasks and on quantitative evaluation of progress, and its willingness to embrace a diverse range of techniques. The importance of software engineering in this context is noted, as are some sociological aspects of the practitioner group.

Two tasks involving lexical semantic sense tagging are described. Different task requirements made it necessary to select different corpora to be tagged and to develop different tagging interfaces to achieve the desired result. A vocabulary-building task required sequential tagging of connnected text, whereas a word-sense identification task required targeted tagging of many instances of common polysemous words. Advantages and drawbacks of both are compared.

The identification and extraction of technical terms is one of the better understood and most robust Natural Language Processing (NLP) technologies within the current state of the art of language engineering. In generic information management contexts, terms have been used primarily for procedures seeking to identify a set of phrases that is useful for tasks such as text indexing, computational lexicology, and machine-assisted translation: such tasks make important use of the assumption that terminology is representative of a given domain. This paper discusses an extension of basic terminology identification technology for the application to two higher level semantic tasks: domain description, the specification of the technical domain of a document, and content characterisation, the construction of a compact, coherent and useful representation of the topical content of a text. With these extensions, terminology identification becomes the foundation of an operational environment for document processing and content abstraction.

This paper focuses on the design methodology of the MultiLingual Dictionary-System (MLDS), which is a human-oriented tool for assisting in the task of translating lexical units, oriented to translators and conceived from studies carried out with translators. We describe the model adopted for the representation of multilingual dictionary-knowledge. Such a model allows an enriched exploitation of the lexical-semantic relations extracted from dictionaries. In addition, MLDS is supplied with knowledge about the use of the dictionaries in the process of lexical translation, which was elicitated by means of empirical methods and specified in a formal language. The dictionary-knowledge along with the task-oriented knowledge are used to offer the translator active, anticipative and intelligent assistance.

In this paper we present results concerning the large scale automatic extraction of pragmatic content from email by a system based on a phrase matching approach to speech act detection combined with empirical detection of speech act patterns in corpora. The results show that most speech acts that occur in this corpus can be recognized by the approach. This investigation is supported by analysis of a corpus consisting of 1000 emails.

We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of information. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.

A probabilistic parameter reestimation algorithm plays a key role in the automatic acquisition of stochastic grammars. In the case of context-free phrase structure grammars, the inside-outside algorithm is widely used. However, it is not directly applicable to Probabilistic Dependency Grammar (PDG), because PDG is not based on constituents but on a head-dependent relation between pairs of words. This paper presents a reestimation algorithm which is a variation of the inside-outside algorithm adapted to probabilistic dependency grammar. The algorithm can be used either to reestimate the probabilistic parameters of an existing dependency grammar, or to extract a PDG from scratch. Using the algorithm, we have learned a PDG from a part-of-speech-tagged corpus of Korean, which showed about 62·82% dependency accuracy (the percentage of correct dependencies) for unseen test sentences.

Many applications need a lexicon that represents semantic information but acquiring lexical information is time consuming. We present a corpus-based bootstrapping algorithm that assists users in creating domain-specific semantic lexicons quickly. Our algorithm uses a representative text corpus for the domain and a small set of ‘seed words’ that belong to a semantic class of interest. The algorithm hypothesizes new words that are also likely to belong to the semantic class because they occur in the same contexts as the seed words. The best hypotheses are added to the seed word list dynamically, and the process iterates in a bootstrapping fashion. When the bootstrapping process halts, a ranked list of hypothesized category words is presented to a user for review. We used this algorithm to generate a semantic lexicon for eleven semantic classes associated with the MUC-4 terrorism domain.

This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate the approach for mixture language modelling. A comparison is made between manual and automatic clustering in order to elucidate how the global content information is expressed in the space. We also compare (in terms of association with manual clustering and language modelling accuracy) alternative term-weighting schemes and the effect of singular value decomposition dimension reduction (latent semantic analysis). Test set perplexity results using the British National Corpus indicate that the approach can improve the potential of statistical language modelling. Using an adaptive procedure, the conventional model may be tuned to track text data with a slight increase in computational cost.

Most parsing algorithms require phrases that are to be combined to be either contiguous or marked as being ‘extraposed’. The assumption that phrases which are to be combined will be adjacent to one another supports rapid indexing mechanisms: the fact that in most languages items can turn up in unexpected locations cancels out much of the ensuing efficiency. The current paper shows how ‘out of position’ items can be incorporated directly. This leads to efficient parsing even when items turn up having been right-shifted, a state of affairs which makes Johnson and Kay's (1994) notion of ‘sponsorship’ of empty nodes inapplicable.

Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be ‘read off’ the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar. In this paper, we explore ways by which a treebank grammar can be reduced in size or ‘compacted’, which involve the use of two kinds of technique: (i) thresholding of rules by their number of occurrences; and (ii) a method of rule-parsing, which has both probabilistic and non-probabilistic variants. Our results show that by a combined use of these two techniques, a probabilistic context-free grammar can be reduced in size by 62% without any loss in parsing performance, and by 71% to give a gain in recall, but some loss in precision.

Verbmobil represents a new generation of speech-to-speech translation systems in which spontaneously spoken language, speaker independence and adaptability as well as the combination of deep and shallow approaches to the analysis and transfer problems are the main features. The project brought together researchers from the fields of signal processing, computational linguistics and artificial intelligence. Verbmobil goes beyond the state-of-the-art in each of these areas, but its main achievement is the seamless integration of them. The first project phase (1993–1996) has been followed up by the second project phase (1997–2000), which aims at applying the results to further languages and at integrating innovative telecooperation techniques. Quite apart from the speech and language processing issues, the size and complexity of the project represent an extreme challenge on the areas of project management and software engineering:

[bull ] 50 researchers from 29 organizations at different sites in different countries are involved in the software development process,

[bull ] to reuse existing software, hardware, knowledge and experience, only a few technical restrictions could be given to the partners.

In this article we describe the Verbmobil prototype system from a software-engineering perspective. We discuss:

[bull ] the modularized functional architecture,

[bull ] the flexible and extensible software architecture which reflects that functional architecture,

[bull ] the evolutionary process of system integration,

[bull ] the communication-based organizational structure of the project,

[bull ] the evaluation of the system operational by the end of the first project phase.

This paper presents a system for automatic verb sense disambiguation using a small corpus and a Machine-Readable Dictionary (MRD) in Korean. The system learns a set of typical uses listed in the MRD usage examples for each of the senses of a polysemous verb in the MRD definitions using verb-object co-occurrences acquired from the corpus. This paper concentrates on the problem of data sparseness in two ways. First, by extending word similarity measures from direct co-occurrences to co-occurrences of co-occurring words, we compute the word similarities using non co-occurring words but co-occurring clusters. Secondly, we acquire IS-A relations of nouns from the MRD definitions. It is possible to roughly cluster the nouns by the identification of the IS-A relationship. Using these methods, two words may be considered similar even if they do not share any word elements. Experiments show that this method can learn from a very small training corpus, achieving over an 86% correct disambiguation performance without any restriction on a word's senses.

In this paper, we describe a context-based method to semantically tag unknown proper nouns (U-PNs) in corpora. Like many others, our system relies on a gazetteer and a set of context-dependent heuristics to classify proper nouns. However, proper nouns are an open-end class: when parsing new fragments of a corpus, even in the same language domain, we can expect that several proper nouns cannot be semantically tagged. The algorithm that we propose assigns to an unknown PN an entity type based on the analysis of syntactically and semantically similar contexts already seen in the application corpus. The performance of the algorithm is evaluated not only in terms of precision, following the tradition of MUC conferences, but also in terms of information gain, an information theoretic measure that takes into account the complexity of the classification task.

In this paper, we describe a case study of a sentence-level categorization in which tagging instructions are developed and used by four judges to classify clauses from the Wall Street Journal as either subjective or objective. Agreement among the four judges is analyzed, and based on that analysis, each clause is given a final classification. To provide empirical support for the classifications, correlations are assessed in the data between the subjective category and a basic semantic class posited by Quirk, Greenbaum, Leech and Svartvik (1985).