Applying named entity recognition and co-reference resolution for segmenting English texts

Fragkou, Pavlina

doi:10.1007/s13748-017-0127-3

Cited by 17 publications

(13 citation statements)

References 29 publications

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“…[ Protein receptor] Protein (IL-2R) alpha chain] Protein gene] DNA NER has drawn considerable attention as the first step towards many natural language processing (NLP) applications including relation extraction (Miwa and Bansal, 2016), event extraction (Feng et al, 2016), co-reference resolution (Fragkou, 2017;Stone and Arora, 2017), and entity linking (Gupta et al, 2017). Much work on NER, however, has ignored nested entities and instead chosen to focus on the non-nested entities, which are also referred to as flat entities.…”

Section: Introductionmentioning

confidence: 99%

Deep Exhaustive Model for Nested Named Entity Recognition

Sohrab¹,

Misawa²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

174

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We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity mentions and classify them with deep neural networks. To reduce the computational costs and capture the information of the contexts around the regions, the model represents the regions using the outputs of shared underlying bidirectional long short-term memory. We evaluate our exhaustive model on the GENIA and JNLPBA corpora in biomedical domain, and the results show that our model outperforms state-of-the-art models on nested and flat NER, achieving 77.1% and 78.4% respectively in terms of F-score, without any external knowledge resources.

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Section: Introductionmentioning

confidence: 99%

Deep Exhaustive Model for Nested Named Entity Recognition

Sohrab¹,

Misawa²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

174

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“…As our future works, we will apply multiple label predication [23] and coreference resolution [24,25] to improve the recall rate of name entity classification, and other classification algorithms will be tested.…”

Section: Discussionmentioning

confidence: 99%

A Two-Step Resume Information Extraction Algorithm

Chen

Zhang

Niu

2018

Mathematical Problems in Engineering

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With the rapid growth of Internet-based recruiting, there are a great number of personal resumes among recruiting systems. To gain more attention from the recruiters, most resumes are written in diverse formats, including varying font size, font colour, and table cells. However, the diversity of format is harmful to data mining, such as resume information extraction, automatic job matching, and candidates ranking. Supervised methods and rule-based methods have been proposed to extract facts from resumes, but they strongly rely on hierarchical structure information and large amounts of labelled data, which are hard to collect in reality. In this paper, we propose a two-step resume information extraction approach. In the first step, raw text of resume is identified as different resume blocks. To achieve the goal, we design a novel feature, Writing Style, to model sentence syntax information. Besides word index and punctuation index, word lexical attribute and prediction results of classifiers are included in Writing Style. In the second step, multiple classifiers are employed to identify different attributes of fact information in resumes. Experimental results on a real-world dataset show that the algorithm is feasible and effective.

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“…One of the most common studied tasks in NLP lies in extracting semantic information from unstructured text in the form of entities and detecting entity mentions across a single document, in particular where the mention is located (its span) and its corresponding classification or entity semantic type, such as person (PER), location (LOC), organization (ORG), etc. The task of entity recognition has long been studied and applied to different higher level tasks such as question answering (Abney et al, 2000), coreference resolution (Fragkou, 2017), relation extraction (Mintz et al, 2009;Miwa and Bansal, 2016;Liu et al, 2017), entity linking (Gupta et al, 2017;Guo and Barbosa, 2014) and event extraction (Feng et al, 2016). Most of the existing work in Named Entity Recognition and Classification focuses on flat mentions, usually corresponding to the longest outer mention (Ling and Weld, 2012;Marcinczuk, 2015;Leaman and Lu, 2016), or using nested mentions that can capture overlapping mentions within different nested levels (Finkel and Manning, 2009;Lu and Roth, 2015;Wang et al, 2018;Ju et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Nested Named Entity Recognition

Marinho¹,

Mendes²,

Miranda³

et al. 2019

Proceedings of the 2nd Clinical Natural Language Processing Workshop

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In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in entity mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further propose a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.

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Applying named entity recognition and co-reference resolution for segmenting English texts

Cited by 17 publications

References 29 publications

Deep Exhaustive Model for Nested Named Entity Recognition

Deep Exhaustive Model for Nested Named Entity Recognition

A Two-Step Resume Information Extraction Algorithm

Hierarchical Nested Named Entity Recognition

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