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Open AccessProceedings ArticleDOI

Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition

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.
Abstract
This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet "so.. kktny in 30 mins?!" -- even human experts find the entity 'kktny' hard to detect and resolve. 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. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.

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Citations
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Journal ArticleDOI

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

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.
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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

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.
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