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
CLIP also Understands Text: Prompting CLIP for Phrase Understanding
TL;DR: It is shown that the text encoder of CLIP actually demonstrates strong ability for phrase understanding, and can even outperform popular language models such as BERT with a properly designed prompt.
Proceedings ArticleDOI
Initial Normalization of User Generated Content: Case Study in a Multilingual Setting
TL;DR: This work targets comment sections of popular Kazakhstani Internet news outlets, where comments almost always appear in Kazakh or Russian, or in a mixture of both, and proposes a simple yet effective normalization method that accounts for multilingual input.
Proceedings ArticleDOI
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing
TL;DR: This work introduces the end-to-end EDIN pipeline that detects, clusters, and indexes mentions of unknown entities in context, and shows that indexing a single embedding per entity unifying the information of multiple mentions works better than indexing mentions indepen-dently.
Journal ArticleDOI
IDRISI-RE: A generalizable dataset with benchmarks for location mention recognition on disaster tweets
TL;DR: IDRISI-RE as mentioned in this paper is a large-scale human-labeled LMR dataset comprising around 20.5k tweets from 19 disaster events of diverse types (e.g., flood and earthquake) covering a wide geographical area of 22 English-speaking countries.
Posted Content
Query Understanding for Natural Language Enterprise Search.
Francisco Borges,Georgios Balikas,Marc Brette,Kempf Guillaume,Arvind Srikantan,Matthieu Landos,Darya Brazouskaya,Qianqian Shi +7 more
TL;DR: The architecture of the NLS system, the particularities of the CRM domain as well as how they have influenced the design decisions are described, and the role of a Deep Learning Named Entity Recognizer is detailed.
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.