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» Exact Decoding for Jointly Labeling and Chunking Sequences
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ACL
2006
13 years 6 months ago
Exact Decoding for Jointly Labeling and Chunking Sequences
There are two decoding algorithms essential to the area of natural language processing. One is the Viterbi algorithm for linear-chain models, such as HMMs or CRFs. The other is th...
Nobuyuki Shimizu, Andrew R. Haas
ACL
2010
13 years 2 months ago
Efficient Staggered Decoding for Sequence Labeling
The Viterbi algorithm is the conventional decoding algorithm most widely adopted for sequence labeling. Viterbi decoding is, however, prohibitively slow when the label set is larg...
Nobuhiro Kaji, Yasuhiro Fujiwara, Naoki Yoshinaga,...
ICML
2004
IEEE
14 years 5 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
EMNLP
2010
13 years 2 months ago
Joint Training and Decoding Using Virtual Nodes for Cascaded Segmentation and Tagging Tasks
Many sequence labeling tasks in NLP require solving a cascade of segmentation and tagging subtasks, such as Chinese POS tagging, named entity recognition, and so on. Traditional p...
Xian Qian, Qi Zhang, Yaqian Zhou, Xuanjing Huang, ...
CVIU
2006
222views more  CVIU 2006»
13 years 4 months ago
Conditional models for contextual human motion recognition
We present algorithms for recognizing human motion in monocular video sequences, based on discriminative Conditional Random Field (CRF) and Maximum Entropy Markov Models (MEMM). E...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...