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2003

Orthographic Case Restoration Using Supervised Learning Without Manual Annotation

12 years 1 months ago
Orthographic Case Restoration Using Supervised Learning Without Manual Annotation
One challenge in text processing is the treatment of case insensitive documents such as speech recognition results. The traditional approach is to re-train a language model excluding case-related features. This paper presents an alternative two-step approach whereby a preprocessing module (Step 1) is designed to restore case-sensitive form to feed the core system (Step 2). Step 1 is implemented as a Hidden Markov Model trained on a large raw corpus of case sensitive documents. It is demonstrated that this approach (i) outperforms the feature exclusion approach for Named Entity tagging, (ii) leads to limited degradation for semantic parsing and relationship extraction, (iii) reduces system complexity, and (iv) has wide applicability: the restored text can feed both statistical model and rule-based systems.
Cheng Niu, Wei Li 0003, Jihong Ding, Rohini K. Sri
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where FLAIRS
Authors Cheng Niu, Wei Li 0003, Jihong Ding, Rohini K. Srihari
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