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Dengjun Ren

dblp:60/6374 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Efficient and distributed learning · 50% Information extraction and text analysis · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › word segmentation
chinese word segmentation		 0.112006
Discriminative Pruning of Language Models for Chinese Word Segmentation · ACL 2006
Machine learning › Efficient and distributed learning › model compression › pruning › DNN pruning
language model pruning		 0.112006
Discriminative Pruning of Language Models for Chinese Word Segmentation · ACL 2006

Methods — techniques the papers use, named apart from their topics

step-by-step growing algorithm · 0.1discriminative pruning · 0.1
YearPublicationVenuePosition
2007 Improving statistical word alignment with various clues
Dengjun Ren, Hua Wu 0003, Haifeng Wang 0001
 MTSummit1
2006 Discriminative Pruning of Language Models for Chinese Word Segmentation
abstract
This paper presents a discriminative pruning method of n-gram language model for Chinese word segmentation. To reduce the size of the language model that is used in a Chinese word segmentation system, importance of each bigram is computed in terms of discriminative pruning criterion that is related to the performance loss caused by pruning the bigram. Then we propose a step-by-step growing algorithm to build the language model of desired size. Experimental results show that the discriminative pruning method leads to a much smaller model compared with the model pruned using the state-of-the-art method. At the same Chinese word segmentation F-measure, the number of bigrams in the model can be reduced by up to 90%. Correlation between language model perplexity and word segmentation performance is also discussed.
Haifeng Wang 0001, Dengjun Ren, Guohua Li
 ACL3