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Using silver and semi-gold standard corpora to compare open named entity recognisers
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Dragon Toolkit: Incorporating Auto-learned Semantic Knowledge into Large-Scale Text Retrieval and Mining
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In: http://www.dragontoolkit.org/dragontoolkit.pdf (2007)
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Topic Signature Language Models for Ad Hoc Retrieval
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In: http://www.daviszhou.net/research/TKDE2007Zhou.pdf (2007)
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A comprehensive comparison study of document clustering for a biomedical digital library medline
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In: http://cci.drexel.edu/faculty/thu/research-papers/FP_52_Yoo.pdf (2006)
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Articulation Modeling for
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In: http://www.daviszhou.net/research/CIKM2007Zhou.pdf (2004)
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Abstract:
Hidden markov model (HMM) is frequently used for Pinyin-to-Chinese conversion. But it only captures the dependency with the preceding character. Higher order markov models can bring higher accuracy, but are computationally unaffordable to average PC settings. We propose a segment-based hidden markov model (SHMM), which has the same magnitude of complexity as firstorder HMM, but generates higher decoding accuracy. SHMM tells a word from a bigram connecting two words, and assigns a reasonable probability to words as a whole. It is more powerful than HMM to decode words containing over two characters. We conduct a comprehensive Pinyin-to-Chinese conversion evaluation on Lancaster corpus. The experiment shows the perfect sentence accuracy is improved from 34.7 % (HMM) to 43.3 % (SHMM). The one-error sentence accuracy is increased from 72.7 % to 78.3%. Furthermore, SHMM can seamlessly integrate with pinyin typing correction, acronym pinyin input, user-defined words, and selfadaptive learning all of which are a must for a commercial Pinyinto-Chinese conversion product in order to improve the efficiency of pinyin input.
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Keyword:
Algorithm; Experimentation; Performance Keywords Chinese Input; Pinyin; Segment-based Hidden Markov Model
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URL: http://www.daviszhou.net/research/CIKM2007Zhou.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.218.7511
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Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval
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In: http://www.cis.drexel.edu/faculty/thu/research-papers/ChenHU-PAKDD09.pdf
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1 Biomedical Ontology MeSH Improves Document Clustering Qualify on MEDLINE Articles: A Comparison Study
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In: http://www.cis.drexel.edu/faculty/thu/research-papers/Yoo_CBMS_2006.pdf
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Using Term Sense to Improve Language Modeling Approach to Genomic IR *
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In: http://cci.drexel.edu/faculty/thu/research-papers/ECIR06_Submitted.pdf
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Utilizing Sentence Similarity and Question Type Similarity to Response to Similar Questions in Knowledge-Sharing Community
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In: http://www.pages.drexel.edu/~pa442/pdf/qaweb2008.pdf
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Mining Hidden Connections among Biomedical Concepts from Disjoint Biomedical Literature Sets through Semantic-Based Association Rule
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In: http://www.daviszhou.net/research/ijis2006hu.pdf
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Integration of Association Rules and Ontology for Semantic-based Query Expansion
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In: http://www.ischool.drexel.edu/dmbio/publication/song_dek_2006.pdf
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Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing
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In: http://www.www2015.it/documents/proceedings/proceedings/p1318.pdf
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