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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To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning
TL;DR: In this article, a method to automatically determine which and how many sources should be exploited is proposed to predict promising sources in low-resource settings, based on model similarity and support vector machines.
Journal ArticleDOI
FiNER: Financial Named Entity Recognition Dataset and Weak-Supervision Model
TL;DR: In this article , a weak-supervision-based NER dataset for the finance domain was developed and tested for the NER task, and the weak-ner framework was extended to make it employable for spanlevel classification.
Proceedings Article
NetEase.AI at SemEval-2023 Task 2: Enhancing Complex Named Entities Recognition in Noisy Scenarios via Text Error Correction and External Knowledge
Zihang Tang,Dong Liu,Jiacheng Li +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed an entity recognition system that integrates text error correction system and external knowledge, which can recognize entities in scenes that contain entities out of knowledge base and text with noise.
Domain Specific Augmentations as Low Cost Teachers for Large Students
TL;DR: In this paper , a framework that uses data augmentation from such domain-specific pretrained models to transfer domain specific knowledge to larger general pre-trained models and improve performance on downstream tasks is introduced.
Proceedings ArticleDOI
TERMinator: A System for Scientific Texts Processing
TL;DR: This paper presents a dataset that includes annotations for two tasks and develops a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction.
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.