This paper introduces a toolkit that allows programmers with no linguistic knowledge to rapidly develop a Spoken Language User Interface (SLUI) for various applications. The applications may vary from web-based e-commerce to the control of domestic appliances. Using the SLUI Toolkit, a programmer is able to create a system that incorporates Natural Language Processing (NLP), complex syntactic parsing, and semantic understanding. The system has been tested using ten human evaluators in a specific domain of a web based e-commerce application. The evaluators have overwhelmingly endorsed the ease of use and applicability of the tool kit in rapid development of speech and natural language processing interfaces for this domain.
With the rapid growth of real world applications for NLP systems, there is a genuine demand for a general toolkit from which programmers with no linguistic knowledge can build specific NLP systems. Such a toolkit should have a parser that is general enough to be used across domains, and yet accurate enough for each specific application. In this paper, we describe a parser that extends a broad-coverage parser, Minipar (Lin, 2001), with an adaptable shallow parser so as to achieve both generality and accuracy in handling domain specific NL problems. We test this parser on our corpus and the results show that the accuracy is significantly higher than a system that uses Minipar alone.
With the rapid growth of real application domains for NLP systems, there is a genuine demand for a general toolkit from which programmers with no linguistic knowledge can build specific NLP systems. Such a toolkit should provide an interface to accept sample sentences and convert them into semantic representations so as to allow programmers to map them to domain actions. In order to reduce the workload of managing a large number of semantic forms individually, the toolkit will perform what we call semantic grouping to organize the forms into meaningful groups. In this paper, we present three semantic grouping methods: similaritybased, verb-based and category-based grouping, and their implementation in the SLUI toolkit. We also discuss the pros and cons of each method and how they can be utilized according to the different domain needs.
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