Recently, most part-of-speech tagging approaches, such as rule-based, probabilistic and neural network approaches, have shown very promising results. In this paper, we are particularly interested in probabilistic approaches, which usually require lots of training data to get reliable probabilities. We alleviate such a restriction of probabilistic approaches by introducing a fuzzy network model to provide a method for estimating more reliable parameters of a model under a small amount of training data. Experiments with the Brown corpus show that the performance of the fuzzy network model is much better than that of the hidden Markov model under a limited amount of training data.

In spite of the wide availability of more powerful (context free, mildly context sensitive, and even Turing-equivalent) formalisms, the bulk of the applied work on language and sublanguage modeling, especially for the purposes of recognition and topic search, is still performed by various finite state methods. In fact, the use of such methods in research labs as well as in applied work actually increased in the past five years. To bring together those developing and using extended finite state methods to text analysis, speech/OCR language modeling, and related CL and NLP tasks with those in AI and CS interested in analyzing and possibly extending the domain of finite state algorithms, a workshop was held in August 1996 in Budapest as part of the European Conference on Artificial Intelligence (ECAI'96).

Operating system command languages assist the user in executing commands for a significant number of common everyday tasks. On the other hand, the introduction of textual command languages for robots has provided the opportunity to perform some important functions that leadthrough programming cannot readily accomplish. However, such command languages assume the user to be expert enough to carry out a specific task in these application domains. On the contrary, a natural language interface to such command languages, apart from being able to be integrated into a future speech interface, can facilitate and broaden the use of these command languages to a larger audience. In this paper, advanced techniques are presented for an adaptive natural language interface that can (a) be portable to a large range of command languages, (b) handle even complex commands thanks to an embedded linguistic parser, and (c) be expandable and customizable by providing the casual user with the opportunity to specify some types of new words as well as the system developer with the ability to introduce new tasks in these application domains. Finally, to demonstrate the above techniques in practice, an example of their application to a Greek natural language interface to the MS-DOS operating system is given.

We discuss the random generation of strings using the grammatical formalism AGFL. This formalism consists of context-free grammars extended with a parameter mechanism, where the parameters range over a finite domain. Our approach consists in static analysis of the combinations of parameter values with which derivations can be constructed. After this analysis, generation of sentences can be performed without backtracking.

This paper addresses the problem of distribution of words and phrases in text, a problem of great general interest and of importance for many practical applications. The existing models for word distribution present observed sequences of words in text documents as an outcome of some stochastic processes; the corresponding distributions of numbers of word occurrences in the documents are modelled as mixtures of Poisson distributions whose parameter values are fitted to the data. We pursue a linguistically motivated approach to statistical language modelling and use observable text characteristics as model parameters. Multi-word technical terms, intrinsically content entities, are chosen for experimentation. Their occurrence and the occurrence dynamics are investigated using a 100-million word data collection consisting of a variety of about 13,000 technical documents. The derivation of models describing word distribution in text is based on a linguistic interpretation of the process of text formation, with the probabilities of word occurrence being functions of observable and linguistically meaningful text characteristics. The adequacy of the proposed models for the description of actually observed distributions of words and phrases in text is confirmed experimentally. The paper has two focuses: one is modelling of the distributions of content words and phrases among different documents; and another is word occurrence dynamics within documents and estimation of corresponding probabilities. Accordingly, among the application areas for the new modelling paradigm are information retrieval and speech recognition.

Language tools that help people with their writing are now usually included in today's word processors. Although these various tools provide increasing support to native speakers of a language, they are much less useful to non-native speakers who are writing in their second language (e.g. French speakers writing in English). Real errors may go undetected and potential errors or non-errors that are flagged by the system may be taken to be genuine errors by the non-native speaker. In this paper, we present the prototype of an English writing tool which is aimed at helping speakers of French write in English. We first discuss the kind of problems non-native speakers have when writing in a second language. We then explain how we collected a corpus of errors which we used to build a typology of errors needed in the various stages of the project. This is followed by an overview of the prototype which contains a number of writing aids (dictionaries, on-line grammar helps, verb conjugator, etc.) and two checking tools: a problem word highlighter which lists all the potentially difficult words that cannot be dealt with correctly by the system (false friends, confusions, etc.) and a grammar checker which detects and corrects morphological and syntactic errors. We describe in detail the automata formalism we use to extract linguistic information, test syntactic environments and detect and correct errors. Finally, we present a first evaluation of the correction capacity of our grammar checker as compared to that of commercially available systems.

This paper describes the key aspects of a parser developed at the University of Pennsylvania from 1958 to 1959. The parser is essentially a cascade of finite state transducers. To the best of our knowledge, this is the first application of finite state transducers to parsing. This parser was recently faithfully reconstructed from the original documentation. Many aspects of this program have a close relationship to some of the recent work on finite state transducers.

Words unknown to the lexicon present a substantial problem to part-of-speech tagging. In this paper we present a technique for fully unsupervised acquisition of rules which guess possible parts of speech for unknown words. This technique does not require specially prepared training data, and uses instead the lexicon supplied with a tagger and word frequencies collected from a raw corpus. Three complimentary sets of word-guessing rules are statistically induced: prefix morphological rules, suffix morphological rules and ending guessing rules. The acquisition process is strongly associated with guessing-rule evaluation methodology which is solely dedicated to the performance of part-of-speech guessers. Using the proposed technique a guessing-rule induction experiment was performed on the Brown Corpus data and rule-sets, with a highly competitive performance, were produced and compared with the state-of-the-art. To evaluate the impact of the word-guessing component on the overall tagging performance, it was integrated into a stochastic and a rule-based tagger and applied to texts with unknown words.

This paper discusses different issues in the construction and knowledge representation of an intelligent dictionary help system. The Intelligent Dictionary Help System (IDHS) is conceived as a monolingual (explanatory) dictionary system for human use (Artola and Evrard, 1992). The fact that it is intended for people instead of automatic processing distinguishes it from other systems dealing with the acquisition of semantic knowledge from conventional dictionaries. The system provides various access possibilities to the data, allowing to deduce implicit knowledge from the explicit dictionary information. IDHS deals with reasoning mechanisms analogous to those used by humans when they consult a dictionary. User level functionality of the system has been specified and a prototype has been implemented (Agirre et al., 1994a). A methodology for the extraction of semantic knowledge from a conventional dictionary is described. The method followed in the construction of the phrasal pattern hierarchies required by the parser (Alshawi, 1989) is based on an empirical study carried out on the structure of definition sentences. The results of its application to a real dictionary has shown that the parsing method is particularly suited to the analysis of short definition sentences, as it was the case of the source dictionary. As a result of this process, the characterization of the different lexical-semantic relations between senses is established by means of semantic rules (attached to the patterns); these rules are used for the initial construction of the Dictionary Knowledge Base (DKB). The representation schema proposed for the DKB (Agirre et al., 1994b) is basically a semantic network of frames representing word senses. After construction of the initial DKB, several enrichment processes are performed on the DKB to add new facts to it; these processes are based on the exploitation of the properties of lexical-semantic relations, and also on specially conceived deduction mechanisms. The result of the enrichment processes show the suitability of the representation schema chosen to deduce implicit knowledge. Erroneous deductions are mainly due to incorrect word sense disambiguation.

We describe new applications of the theory of automata to natural language processing: the representation of very large scale dictionaries and the indexation of natural language texts. They are based on new algorithms that we introduce and describe in detail. In particular, we give pseudocodes for the determinisation of string to string transducers, the deterministic union of p-subsequential string to string transducers, and the indexation by automata. We report on several experiments illustrating the applications.

In this article, we describe AIMS (Assisted Indexing at Mississippi State), a system intended to aid human document analysts in the assignment of indexes to physical chemistry journal articles. The two major components of AIMS are a natural language processing (NLP) component and an index generation (IG) component. We provide an overview of what each of these components does and how it works. We also present the results of a recent evaluation of our system in terms of recall and precision. The recall rate is the proportion of the ‘correct’ indexes (i.e. those produced by human document analysts) generated by AIMS. The precision rate is the proportion of the generated indexes that is correct. Finally, we describe some of the future work planned for this project.

Finite automata and various extensions of them, such as transducers, are used in areas as diverse as compilers, spelling checking, natural language grammar checking, communication protocol design, digital circuit simulation, digital flight control, speech recognition and synthesis, genetic sequencing, and Java program verification. Unfortunately, as the number of applications has grown, so has the variety of implementations and implementation techniques. Typically, programmers will be confused enough to resort to their text books for the most elementary algorithms. Recently, advances have been made in taxonomizing algorithms for constructing and minimizing automata and in evaluating various implementation strategies Watson 1995. Armed with this, a number of general-purpose toolkits have been developed at universities and companies. One of these, FIRE Lite, was developed at the Eindhoven University of Technology, while its commercial successor, FIRE Engine II, has been developed at Ribbit Software Systems Inc. Both of these toolkits provide implementations of all of the known algorithms for constructing automata from regular expressions, and all of the known algorithms for minimizing deterministic finite automata. While the two toolkits have a great deal in common, we will concentrate on the structure and use of the noncommercial FIRE Lite. The prototype version of FIRE Lite was designed with compilers in mind. More recently, computation linguists and communications protocol designers have become interested in using the toolkit. This has led to the development of a much more general interface to FIRE Lite, including the support of both Mealy and Moore regular transducers. While such a toolkit may appear extremely complex, there are only a few choices to be made. We also consider a ‘recipe’ for making good use of the toolkits. Lastly, we consider the future of FIRE Lite. While FIRE Engine II has obvious commercial value, we are committed to maintaining a version which is freely available for academic use.

Researchers and industry are actively developing Software Agents (SAs), autonomous software that will assist users in achieving various tasks, collaborate with them, or even act on their behalf. To explore new interaction modes for SAs which need to be more sophisticated than simple exchanges of messages, we have analysed human conversations and elaborated an interaction approach for SAs based on a conversation model. Using this approach, we have developed a multi-agent system that simulates conversations involving SAs. We assume that SAs perform communicative acts to negotiate about mental states, such as beliefs and goals, turn-taking and special conversational sequences. We also assume that SAs respect communication protocols when they negotiate. In this paper, we describe the conceptual structure of communicative acts, the knowledge structures used to model a conversation, and the communication protocols. We show how an inference engine using ‘conversation-managing rules’ can be integrated in a conversational agent responsible for interpreting communicative acts, and we discuss the different kinds of rules that we propose. The prototype PSICO was implemented to simulate conversations on a computer platform.

The full paper describes an environment for the generation of non-deterministic taggers, currently used for the development of a Spanish lexicon. In relation to previous approaches, our system includes the use of verification tools in order to assure the robustness of the generated taggers. A wide variety of user defined criteria can be applied for checking the exact properties of the system.

This paper describes NL-OOPS, a CASE tool that supports requirements analysis by generating object oriented models from natural language requirements documents. The full natural language analysis is obtained using as a core system the Natural Language Processing System LOLITA. The object oriented analysis module implements an algorithm for the extraction of the objects and their associations for use in creating object models.

Many of the processing steps in natural language engineering can be performed using finite state transducers. An optimal way to create such transducers is to compile them from regular expressions. This paper is an introduction to the regular expression calculus, extended with certain operators that have proved very useful in natural language applications ranging from tokenization to light parsing. The examples in the paper illustrate in concrete detail some of these applications.