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TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing ...
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SpanNER: Named Entity Re-/Recognition as Span Prediction ...
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Align Voting Behavior with Public Statements for Legislator Representation Learning ...
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fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP ...
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{K-Adapter}: {I}nfusing {K}nowledge into {P}re-{T}rained {M}odels with {A}dapters ...
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Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ...
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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Classifying Dyads for Militarized Conflict Analysis
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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A Bayesian Framework for Information-Theoretic Probing
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters ...
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A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 78-92 (2020) (2020)
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GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge ...
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Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning ...
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Abstract:
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity. This greatly reduces the requirement on the quality of the dictionaries and makes our method generalize well with quite simple dictionaries. Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. We have published the source code at \url{https://github.com/v-mipeng/LexiconNER}. ... : to appear at ACL 2019 (revise expression of equation (4)) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1906.01378 https://dx.doi.org/10.48550/arxiv.1906.01378
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