Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition
Leon Derczynski,Eric Nichols,Marieke van Erp,Nut Limsopatham +3 more
- pp 140-147
TLDR
The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities and to evaluate the ability of participating entries to detect and classify novel and emerging named entities in noisy text.Citations
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Journal ArticleDOI
Extracting psychiatric stressors for suicide from social media using deep learning
Jingcheng Du,Yaoyun Zhang,Jianhong Luo,Jianhong Luo,Yuxi Jia,Yuxi Jia,Qiang Wei,Cui Tao,Hua Xu +8 more
TL;DR: This is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches and transfer learning strategy which leverages an existing annotation dataset from clinical text to reduce the annotation cost and improve the performance.
Proceedings ArticleDOI
A Survey of Current Datasets for Code-Switching Research
TL;DR: A set of quality metrics to evaluate the dataset and categorize them accordingly is proposed and will assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc.
Posted Content
BERTweet: A pre-trained language model for English Tweets
TL;DR: BERTweet is presented, the first public large-scale pre-trained language model for English Tweets, trained using the RoBERTa pre-training procedure, producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks.
Proceedings ArticleDOI
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input
TL;DR: GEMNET is proposed, a novel approach for gazetteer knowledge integration, including a flexible Contextual Gazetteer Representation encoder that can be fused with any word-level model; and a Mixture-of- Experts gating network that overcomes the feature overuse issue by learning to conditionally combine the context and gazetteser features, instead of assigning them fixed weights.
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Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries
TL;DR: Three entity representations that allow LMs to handle millions of entities are explored and a detailed case study on paraphrased querying of facts stored in LMs is presented, thereby providing a proof-of-concept that language models can indeed serve as knowledge bases.
References
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Proceedings ArticleDOI
The Stanford CoreNLP Natural Language Processing Toolkit
Christopher D. Manning,Mihai Surdeanu,John Bauer,Jenny Rose Finkel,Steven Bethard,David McClosky +5 more
TL;DR: The design and use of the Stanford CoreNLP toolkit is described, an extensible pipeline that provides core natural language analysis, and it is suggested that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.
Book
Naming and Necessity
TL;DR: In this paper, the authors make a connection between the mind-body problem and the so-called "identity thesis" in analytic philosophy, which has wide-ranging implications for other problems in philosophy that traditionally might be thought far-removed.
Proceedings ArticleDOI
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
TL;DR: The CoNLL-2003 shared task on NER as mentioned in this paper was the first NER task with language-independent named entity recognition (NER) data sets and evaluation method, and a general overview of the systems that participated in the task and their performance.
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NLTK: The Natural Language Toolkit
Edward Loper,Steven Bird +1 more
TL;DR: NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware that covers symbolic and statistical natural language processing.