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Named Entity Recognition (NER)

104 papers with code · Natural Language Processing

Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Example:

Mark Watney visited Mars
B-PER I-PER O B-LOC

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Latest papers without code

DATNet: Dual Adversarial Transfer for Low-resource Named Entity Recognition

ICLR 2019 Joey Tianyi Zhou et al

We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).

NAMED ENTITY RECOGNITION (NER)

01 May 2019

Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

9 Apr 2019Angli Liu et al

Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB.

LANGUAGE MODELLING NAMED ENTITY RECOGNITION (NER)

09 Apr 2019

Effective Context and Fragment Feature Usage for Named Entity Recognition

5 Apr 2019Nargiza Nosirova et al

In this paper, we explore a new approach to named entity recognition (NER) with the goal of learning from context and fragment features more effectively, contributing to the improvement of overall recognition performance.

NAMED ENTITY RECOGNITION (NER) WORD EMBEDDINGS

05 Apr 2019

A Multi-task Learning Approach for Named Entity Recognition using Local Detection

5 Apr 2019Nargiza Nosirova et al

As a result, we observed competitive performance in nearly all of the tasks.

MULTI-TASK LEARNING NAMED ENTITY RECOGNITION (NER)

05 Apr 2019

CAN-NER: Convolutional Attention Network forChinese Named Entity Recognition

3 Apr 2019Yuying Zhu et al

Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters.

CHINESE WORD SEGMENTATION NAMED ENTITY RECOGNITION (NER)

03 Apr 2019

Using Similarity Measures to Select Pretraining Data for NER

1 Apr 2019Xiang Dai et al

Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks.

NAMED ENTITY RECOGNITION (NER)

01 Apr 2019

CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor

29 Mar 2019Xiaohui Zhao et al

Extracting key information from documents, such as receipts or invoices, and preserving the interested texts to structured data is crucial in the document-intensive streamline processes of office automation in areas that includes but not limited to accounting, financial, and taxation areas.

NAMED ENTITY RECOGNITION (NER)

29 Mar 2019

In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse

28 Mar 2019Mahmoud El-Haj et al

We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse.

NAMED ENTITY RECOGNITION (NER) WORD SENSE DISAMBIGUATION

28 Mar 2019

ner and pos when nothing is capitalized

27 Mar 2019Stephen Mayhew et al

While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.

MACHINE TRANSLATION NAMED ENTITY RECOGNITION (NER) PART-OF-SPEECH TAGGING

27 Mar 2019

Cloze-driven Pretraining of Self-attention Networks

19 Mar 2019Alexei Baevski et al

We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems.

CONSTITUENCY PARSING NAMED ENTITY RECOGNITION (NER) SENTIMENT ANALYSIS TEXT CLASSIFICATION

19 Mar 2019