Automatic Semantic Grouping in a Spoken Language User Interface Toolkit

Automatic Semantic Grouping in a Spoken Language User Interface Toolkit Hassan Alam, Hua Cheng, Rachmat Hartono, Aman Kumar, Paul Llido, Crystal Nakatsu, Huy Nguyen, Fuad Rahman, Yuliya Tarnikova, Timotius Tjahjadi and Che Wilcox BCL Technologies Inc. Santa Clara, CA 95050 U.S.A. fuad@bcltechnologies.com Abstract 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: similarity- based, 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. 1 Introduction and Motivation With the improvement of natural language processing (NLP) and speech recognition techniques, spoken language will become the input of choice for software user interfaces, as it is the most natural way of communication. In the mean time, the domains for NLP systems, especially those handling speech input, have grown rapidly in recent years. However, most computer programmers do not have enough linguistic knowledge to develop an NLP system to handle speech input. There is a genuine demand for a general toolkit from which programmers with no linguistic knowledge can rapidly build speech based NLP systems to handle their domain specific problems more accurately (Alam, 2000). The toolkit will allow programmers to generate Spoken Language User Interface (SLUI) front ends for new and existing applications using, for example, a program-through-example method. In this methodology, the programmer will specify a set of sample input sentences or a domain corpus for each task. The toolkit will then organize the sentences by meaning and even generate a large set of syntactic variations for a given sentence. It will also generate the code that takes a user’s spoken request and executes a command on an application. This methodology is similar to using a GUI toolkit to develop a graphical user interface so that programmers can develop GUI without learning graphics programming. Currently this is an active research area, and the present work is funded by the Advanced Technology Program (ATP) of the National Institute of Standards and Technology (NIST). In the program-through-example approach, the toolkit should provide an interface for the programmers to input domain specific corpora and then process the sentences into semantic representations so as to capture the semantic meanings of the sentences. In a real world application, this process results in a large number of semantic forms. Since the programmers have to manually build the links between these forms and their specific domain actions, they are likely to be overwhelmed …

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