A survey on recent advances in named entity recognition from deep learning models
Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have been studied …
answering, information retrieval, relation extraction, etc. NER systems have been studied …
Named entity recognition and classification in historical documents: A survey
After decades of massive digitisation, an unprecedented number of historical documents are
available in digital format, along with their machine-readable texts. While this represents a …
available in digital format, along with their machine-readable texts. While this represents a …
Sentiment analysis of political communication: Combining a dictionary approach with crowdcoding
M Haselmayer, M Jenny - Quality & quantity, 2017 - Springer
Sentiment is important in studies of news values, public opinion, negative campaigning or
political polarization and an explosive expansion of digital textual data and fast progress in …
political polarization and an explosive expansion of digital textual data and fast progress in …
[PDF][PDF] A boundary-aware neural model for nested named entity recognition
In natural language processing, it is common that many entities contain other entities inside
them. Most existing works on named entity recognition (NER) only deal with flat entities but …
them. Most existing works on named entity recognition (NER) only deal with flat entities but …
Named entity recognition and classification on historical documents: A survey
After decades of massive digitisation, an unprecedented amount of historical documents is
available in digital format, along with their machine-readable texts. While this represents a …
available in digital format, along with their machine-readable texts. While this represents a …
SkillSpan: Hard and soft skill extraction from English job postings
Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor
market dynamics. However, there is a lacuna of datasets and annotation guidelines; …
market dynamics. However, there is a lacuna of datasets and annotation guidelines; …
A Finnish news corpus for named entity recognition
T Ruokolainen, P Kauppinen, M Silfverberg… - Language Resources …, 2020 - Springer
We present a corpus of Finnish news articles with a manually prepared named entity
annotation. The corpus consists of 953 articles (193,742 word tokens) with six named entity …
annotation. The corpus consists of 953 articles (193,742 word tokens) with six named entity …
[HTML][HTML] Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction
Abstract Background and Objectives: Named Entity Recognition (NER) and Relation
Extraction (RE) are two of the most studied tasks in biomedical Natural Language …
Extraction (RE) are two of the most studied tasks in biomedical Natural Language …
Fine-grained named entity recognition in legal documents
This paper describes an approach at Named Entity Recognition (NER) in German language
documents from the legal domain. For this purpose, a dataset consisting of German court …
documents from the legal domain. For this purpose, a dataset consisting of German court …
MasakhaNER: Named entity recognition for African languages
We take a step towards addressing the under-representation of the African continent in NLP
research by bringing together different stakeholders to create the first large, publicly …
research by bringing together different stakeholders to create the first large, publicly …