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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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Proceedings ArticleDOI

SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)

TL;DR: The SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2) as discussed by the authors focused on methods to identify complex finegrained named entities across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings.
Journal ArticleDOI

DWIE : an entity-centric dataset for multi-task document-level information extraction

TL;DR: This paper proposes a new entity-driven metric that takes into account the number of mentions that compose each of the predicted and ground truth entities in DWIE, and proposes to use graph-based neural message passing techniques between document-level mention spans to stimulate further research in graph neural networks for representation learning in multi-task IE.
Proceedings ArticleDOI

Named Entity Recognition for Social Media Texts with Semantic Augmentation

TL;DR: This paper proposed a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account, and proposed an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.
Posted Content

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

TL;DR: This paper improves NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactical information, respectively.
Posted Content

Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER

TL;DR: This paper formally shows the limitation of (CRF-)BiLSTM in modeling cross-context patterns for each word – the XOR limitation, and shows that two types of simple cross-structures – self-attention and Cross-BiL STM – can effectively remedy the problem.
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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