Poster: Using Prior Domain Knowledge to Build HMM-Based Semantic Tagger Trained on Completely Unannotated Data

author: Kinfe Tadesse Mengistu, University of Magdeburg
published: Aug. 11, 2008,   recorded: July 2008,   views: 137

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Description

In this paper, we propose a robust statistical semantic tagging model trained on completely unannotated data. The approach relies mainly on prior domain knowledge to counterbalance the lack of semantically annotated treebank data. The proposed method encodes longer contextual information by grouping strongly related semantic concepts together into cohesive units. The method is based on hidden Markov model (HMM) and offers high ambiguity resolution power, outputs semantically rich information, and requires relatively low human effort. The approach yields high-performance models that are evaluated on two different corpora in two application domains in English and German.

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Reviews and comments:

Comment1 Binyam Tewolde, April 12, 2009 at 10:10 a.m.:

Hi Kinfe how are you doing bro. I'm proud of you


Comment2 Ayalew Alemayehu, April 25, 2009 at 4:57 p.m.:

hi kinfe did u remember me. i see ur paper and i am happy to see this kind ofresearch and good paper.

Ayalew

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