Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
An algorithm for suffix stripping
Readings in information retrieval
Experiments and prospects of Example-Based Machine Translation
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
| Hi-index | 0.01 |
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.