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.

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 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.

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.