Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

Navigation and interaction in a real-scale digital mock-up using natural language and user gesture

This paper tries to demonstrate a very new real-scale 3D system and sum up some firsthand and cutting edge results concerning multi-modal navigation and interaction interfaces. This work is part of the CALLISTO-SARI collaborative project. It aims at constructing an immersive room, developing a set of software tools and some navigation/interaction interfaces. Two sets of interfaces will be introduced here: 1) interaction devices, 2) natural language (speech processing) and user gesture. The survey on this system using subjective observation (Simulator Sickness Questionnaire, SSQ) and objective measurements (Center of Gravity, COG) shows that using natural languages and gesture-based interfaces induced less cyber-sickness comparing to device-based interfaces. Therefore, gesture-based is more efficient than device-based interfaces.FUI CALLISTO-SAR

Dealing with natural language interfaces in a geolocation context

In the geolocation field where high-level programs and low-level devices coexist, it is often difficult to find a friendly user inter- face to configure all the parameters. The challenge addressed in this paper is to propose intuitive and simple, thus natural lan- guage interfaces to interact with low-level devices. Such inter- faces contain natural language processing and fuzzy represen- tations of words that facilitate the elicitation of business-level objectives in our context

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems