When trying to identify essential concepts and relationships in a medium-size corpus, it is not always possible to rely on statistical methods, as the frequencies are too low. We present an alternative method, symbolic, based on the simplification of parse trees. We discuss the re-suits on nominal phrases of two technical corpora, analyzed by two different robust parsers used for terminology up...

In this paper we introduce a word clustering algorithm which uses a bilingual, parallel corpus to group together words in the source and target language. Our method generalizes previous mutual information clustering algorithms for monolingual data by incorporating a statistical translation model. Preliminary experiments have shown that the algorithm can e ectively employ the constraints implici...

Building a stochastic language model (LM) for speech recognition requires a large corpus of target tasks. For some tasks no enough large corpus is available and this is an obstacle to achieving high recognition accuracy. In this paper, we propose a method for building an LM with a higher prediction power using large corpora from different tasks rather than an LM estimated from a small corpus fo...

In this paper, we propose a word confusion network (WCN) based approach to perform clustering of the spoken documents and analyze its ability to handle the influence of speech recognition errors. WCN compactly represents multiple confidence weighted recognition hypotheses. Thus it provides scope for improving the clustering accuracy as a result of the likely presence of the correct transcriptio...