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.

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 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.

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.

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.

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.

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.

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.