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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A Survey of Knowledge-Enhanced Pre-trained Language Models
TL;DR: This paper presented a comprehensive review of knowledge-enhanced pre-trained language models (KE-PLMs) to provide a clear insight into this thriving field and introduced appropriate taxonomies for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight these two main tasks of NLP.
Proceedings ArticleDOI
PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks
TL;DR: This work explores Peer (P R) relation, which indicates that the two spans are two different instances from the same category sharing similar features, and proposes a novel Peer D ata A ugmentation (PeerDA) approach to treat span-span pairs with the P R relation as a kind of augmented training data.
Journal ArticleDOI
Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map
TL;DR: A postprocessing method for correcting machine translation outputs using a named entity recognition (NER) model to overcome the problem of OOV words in NMT tasks and improves the bilingual evaluation understudy (BLEU) score.
Proceedings ArticleDOI
Infrrd.ai at SemEval-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and EnsembleCRF
TL;DR: The proposed method shows that the ensemble of models with a multilingual language model as the base with the help of an encoder performs better than a single language-specific model.
Journal ArticleDOI
Endowing Language Models with Multimodal Knowledge Graph Representations
TL;DR: This work uses the recently released VisualSem KG as an external knowledge repository, which covers a subset of Wikipedia and WordNet entities, and compares a mix of tuple-based and graph-based algorithms to learn entity and relation representations that are grounded on the KG multimodal information.
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.
Posted Content
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.