Improved Sequential Dependency Analysis Integrating Labeling-Based Sentence Boundary Detection

Takanobu OBA  Takaaki HORI  Atsushi NAKAMURA  

IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.5   pp.1272-1281
Publication Date: 2010/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.1272
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Natural Language Processing
sequential dependency analysis,  labeling,  CRF,  sentence boundary detection,  

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A dependency structure interprets modification relationships between words or phrases and is recognized as an important element in semantic information analysis. With the conventional approaches for extracting this dependency structure, it is assumed that the complete sentence is known before the analysis starts. For spontaneous speech data, however, this assumption is not necessarily correct since sentence boundaries are not marked in the data. Although sentence boundaries can be detected before dependency analysis, this cascaded implementation is not suitable for online processing since it delays the responses of the application. To solve these problems, we proposed a sequential dependency analysis (SDA) method for online spontaneous speech processing, which enabled us to analyze incomplete sentences sequentially and detect sentence boundaries simultaneously. In this paper, we propose an improved SDA integrating a labeling-based sentence boundary detection (SntBD) technique based on Conditional Random Fields (CRFs). In the new method, we use CRF for soft decision of sentence boundaries and combine it with SDA to retain its online framework. Since CRF-based SntBD yields better estimates of sentence boundaries, SDA can provide better results in which the dependency structure and sentence boundaries are consistent. Experimental results using spontaneous lecture speech from the Corpus of Spontaneous Japanese show that our improved SDA outperforms the original SDA with SntBD accuracy providing better dependency analysis results.