@inproceedings{87155dd2ea4a49bd816ce120c2d794d0,
title = "An integrated deterministic and nondeterministic inference algorithm for sequential labeling",
abstract = "In this paper, we present a new search algorithm for sequential labeling tasks based on the conditional Markov models (CMMs) frameworks. Unlike conventional beam search, our method traverses all possible incoming arcs and also considers the {"}local best{"} so-far of each previous node. Furthermore, we propose two heuristics to fit the efficiency requirement. To demonstrate the effect of our method, six variant and large-scale sequential labeling tasks were conducted in the experiment. In addition, we compare our method to Viterbi and Beam search approaches. The experimental results show that our method yields not only substantial improvement in runtime efficiency, but also slightly better accuracy. In short, our method achieves 94.49 F (β) rate in the well-known CoNLL-2000 chunking task.",
keywords = "L-regularization, machine learning, part-of-speech tagging, support vector machines",
author = "Wu, {Yu Chieh} and Lee, {Yue Shi} and Yang, {Jie Chi} and Yen, {Show Jane}",
year = "2010",
doi = "10.1007/978-3-642-17187-1_21",
language = "???core.languages.en_GB???",
isbn = "3642171869",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "221--230",
booktitle = "Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings",
note = "6th Asia Information Retrieval Societies Conference, AIRS 2010 ; Conference date: 01-12-2010 Through 03-12-2010",
}