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Learning Tag Dependencies for Sequence Tagging
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. ...
Sequence tagging is the basis for multiple applications in natural language processing. ...
We thank the anonymous reviewers for their constructive comments. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ...
doi:10.24963/ijcai.2018/637
dblp:conf/ijcai/ZhangCZLY18
fatcat:tpzgokv5ereavio4u57tftsc2i
Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging
[article]
1994
arXiv
pre-print
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A fast, incremental implementation and a mechanism for recording the dependencies that underlie the resulting rule sequence are also described. ...
The method learns a series of symbolic transformational rules, which can then be applied in sequence to a test corpus to produce predictions. ...
We point out how it manages to largely avoid di culties with overtraining, and show a way of recording the dependencies between rules in the learned sequence. ...
arXiv:cmp-lg/9406011v1
fatcat:zxwnqubbgreubkzgxdydydssb4
Easy-First POS Tagging and Dependency Parsing with Beam Search
2013
Annual Meeting of the Association for Computational Linguistics
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In this paper, we combine easy-first dependency parsing and POS tagging algorithms with beam search and structured perceptron. ...
On CTB, we achieve 94.01% tagging accuracy and 86.33% unlabeled attachment score with a relatively small beam width. On PTB, we also achieve state-of-the-art performance. ...
Research Funds for the Central Universities (N100204002). ...
dblp:conf/acl/MaZXY13
fatcat:rpoeg25xtzbbtdvv5jgrqbrkaq
Investigating NP-Chunking with Universal Dependencies for English
2018
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)
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We then demonstrate how the task of NP-chunking can benefit PoS-tagging in a multi-task learning setting -comparing two different strategies -and how it can be used as a feature for dependency parsing ...
in order to learn enriched models. ...
Acknowledgment The author would like to thank the anonymous reviewers for their comments and suggestions, and add as well a special thanks to her colleagues from the Data Science team at Siteimprove. ...
doi:10.18653/v1/w18-6010
dblp:conf/acludw/Lacroix18
fatcat:bniccpxqurdvtlocasiei3lmqy
Neural sequence labeling for Vietnamese POS Tagging and NER
[article]
2018
arXiv
pre-print
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This paper presents a neural architecture for Vietnamese sequence labeling tasks including part-of-speech (POS) tagging and named entity recognition (NER). ...
Experiments on benchmark datasets show that this work achieves state-of-the-art performances on both tasks - 93.52% accuracy for POS tagging and 94.88% F1 for NER. Our sourcecode is available at here. ...
We would like to thank the NNVLP [22] team for publishing the pre-trained word embedding set that we used during training and evaluating stage of our model. ...
arXiv:1811.03754v2
fatcat:a6c6cdosyjhdbhqpwhpu73242q
Sequence Tagging for Fast Dependency Parsing
2019
Proceedings (MDPI)
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In this study we adopt a radically different approach and cast full dependency parsing as a pure sequence tagging task. ...
In particular, we apply a linearization function to the tree that results in an output label for each token that conveys information about the word's dependency relations. ...
In a similar fashion, we propose to apply sequence tagging models for dependency parsing [8] , using NCRF++ [9] as our sequence tagging framework. ...
doi:10.3390/proceedings2019021049
fatcat:l5e4ohej7vasflamvnjl2l6zku
Natural Language Processing Tools for Tamil Grammar Learning and Teaching
2010
International Journal of Computer Applications
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Tools like Character Analyzer for analyzing character, Morphological Analyzer and Generator and Verb Conjugator for the word level analysis and Parts of Speech Tagger, Chunker and Dependency parser for ...
In this paper we present the Grammar teaching tools for analyzing and learning character, word and sentence of Tamil Language. ...
CRFs are used for sequence tagging tasks where a sequence of words must be annotated with a sequence of labels, one per word. ...
doi:10.5120/1314-1790
fatcat:oupplfz5ubc65fuilgqr2bnmo4
Deep Learning for Character-Based Information Extraction
[chapter]
2014
Lecture Notes in Computer Science
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characters; (2) abundant online sequences (unlabeled) are utilized to improve the vector representation through semi-supervised learning; and (3) the constraints of spatial dependency among output labels ...
on protein sequences. ...
Improving Representation Learning with Unlabeled Sequences Manually labeling character-based sequences, i.e. to obtain tag label for each character, could be quite time-consuming, since it requires very ...
doi:10.1007/978-3-319-06028-6_74
fatcat:ubzh4ytyrngapdfnijcizhysmi
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models
2017
Proceedings of the First Workshop on Subword and Character Level Models in NLP
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It has recently been applied for crosslingual transfer learning for paradigm completion-the task of producing inflected forms of lemmata-with sequenceto-sequence networks. ...
To investigate this, we propose a set of data-dependent experiments using an existing encoder-decoder recurrent neural network for the task. ...
Acknowledgments We would like to thank Hinrich Schütze and the anonymous reviewers for their helpful comments. ...
doi:10.18653/v1/w17-4110
dblp:conf/emnlp/JinK17
fatcat:vi2as53gkrb45hut7jbwh2tip4
Embedded-State Latent Conditional Random Fields for Sequence Labeling
2018
Proceedings of the 22nd Conference on Computational Natural Language Learning
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While RNNs have provided increasingly powerful context-aware local features for sequence tagging, they have yet to be integrated with a global graphical model of similar expressivity in the output distribution ...
an embedding space for hidden states. ...
In this work, while using state-of-the-art sequence tagging baselines for input representation learning, we concern ourselves with learning the global structure of the output space of label sequences, ...
doi:10.18653/v1/k18-1001
dblp:conf/conll/ThaiRMVM18
fatcat:nv2jel7skfbrvmm4s7qr6jevum
Embedded-State Latent Conditional Random Fields for Sequence Labeling
[article]
2018
arXiv
pre-print
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While RNNs have provided increasingly powerful context-aware local features for sequence tagging, they have yet to be integrated with a global graphical model of similar expressivity in the output distribution ...
an embedding space for hidden states. ...
In this work, while using state-of-the-art sequence tagging baselines for input representation learning, we concern ourselves with learning the global structure of the output space of label sequences, ...
arXiv:1809.10835v1
fatcat:s6tb4cd2ujaidlgfgtjbjmur2m
Multi-Label Community-Based Question Classification via Personalized Sequence Memory Network Learning
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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capture the high-order tag dependency. ...
We introduce the personalized sequence memory network that leverages not only the semantics of questions but also the personalized information of askers to provide the sequence tag learning function to ...
Acknowledgement This work was supported by NSFC (U1611461), Key Program of Zhejiang Province (2015C01027), China Knowledge Center for Engineering and Microsoft Research Asia. ...
doi:10.1609/aaai.v32i1.12171
fatcat:cl6kewoyrfbxtffud6rkkhfez4
Discriminative Training of Sequence Taggers via Local Feature Matching
2014
International Journal of Fuzzy Logic and Intelligent Systems
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Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. ...
For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators. ...
Conclusions In this paper, we proposed a novel parameter learning method for CRFs to tackle the sequence tagging problem. ...
doi:10.5391/ijfis.2014.14.3.209
fatcat:koeopsvynve7hizcx74ebwbk7i
Viable Dependency Parsing as Sequence Labeling
2019
Proceedings of the 2019 Conference of the North
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We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). ...
The results across the board show that on average MTL models with auxiliary losses for constituency parsing outperform single-task ones by 1.14 F1 points, and for dependency parsing by 0.62 UAS points. ...
We gratefully acknowledge NVIDIA Corporation for the donation of a GTX Titan X GPU. amount of non-projectivity (BIST is a projective parser). ...
doi:10.18653/v1/n19-1077
dblp:conf/naacl/StrzyzVG19
fatcat:hnfrw22lhjggjjymceno4cat2e
Weakly Supervised Sequence Tagging from Noisy Rules
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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We propose a framework for training sequence tagging models with weak supervision consisting of multiple heuristic rules of unknown accuracy. ...
In addition to supporting rules that vote on tags in the output sequence, we introduce a new type of weak supervision, called linking rules, that vote on how sequence elements should be grouped into spans ...
Acknowledgements We thank Xufan Zhang for helpful discussions on identifiability in Bayesian networks. ...
doi:10.1609/aaai.v34i04.6009
fatcat:xxrrl2xnrrgtddlfihp6hce4ra
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