Topic Segmentation with Hybrid Document Indexing

We present a domain-independent unsupervised topic segmentation approach based on hybrid document indexing. Lexical chains have been successfully employed to evaluate lexical cohesion of text segments and to predict topic boundaries. Our approach is based in the notion of semantic cohesion. It uses spectral embedding to estimate semantic association between content nouns over a span of multiple text segments. Our method significantly outperforms the baseline on the topic segmentation task and achieves performance comparable to state-of-the-art methods that incorporate domain specific information.

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Irina Matveeva, Gina-Anne Levow
• Keywords: EMNLP 2007, Natural Language Processing, Text Segments, Topic Segmentation, Topic Segmentation Approach