Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing

Zhu, Fei; Shen, Bairong

doi:10.1371/journal.pone.0039230

Cited by 27 publications

(13 citation statements)

References 22 publications

(40 reference statements)

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“…Although the results obtained by (Zhu, 2016) is better than that obtained here, the proposed EDRWE has two main contributions. First, EDRWE proves to be suitable when only small training data is available.…”

Section: Resultscontrasting

confidence: 59%

Untitled

2017

APJITM

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Biomedical Named Entity Recognition (Bio-NER) is an essential step of biomedical information extraction and biomedical text mining. Although, a lot of researches have been made in the design of rule-based and supervised tools for general NER, Bio-NER still remains a challenge and an area of active research, as still there is huge difference in F-score of 10 points between general newswire NER and Bio-NER. The complex structures of the biomedical entities pose a huge challenge for their recognition. To handle this, this paper explores different effective word representations with Support Vector Machine (SVM) to deal with the complex structures of biomedical named entities. First, this paper identifies and evaluates a set of morphological and contextual features with SVM learning method for Bio-NER. This paper also presents an extended distributed representation word embedding technique (EDRWE) for Bio-NER. These models are evaluated on widely used standard Bio-NER dataset namely GENIA corpus. Experimental results show that EDRWE technique improves the overall performance of the Bio-NER and outperforms all other representation methods. Results analysis shows that the new EDRWE is satisfactory and effective for Bio-NER especially when only a small-sized data set is available.

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Section: Resultscontrasting

confidence: 59%

Untitled

2017

APJITM

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“…It presents an improvement of 3.12% over the second best system we compared, Zhu et al (2012) which achieved the F-measure of 89.0% on GENIA corpus for PNE identification task [34].…”

Section: Resultsmentioning

confidence: 80%

Protein Named Entity Identification Based on Probabilistic Features Derived from GENIA Corpus and Medical Text on the Web

Sumathipala

Yamada

Unehara

2015

Int. J. Fuzzy Log. Intell. Syst.

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Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

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“…Recent successes have been found in systems utilizing ensemble classifiers and models. Zhu and Shen combined support vector machine and CRF approaches (Zhu & Shen, 2012). Habibi et al proposed an approach, long short-term memory network (LSTM), using both deep neuron network and statistical models (Habibi et al, 2017).…”

Section: Entity Recognition Methods and Toolsmentioning

confidence: 99%

Improving Publication Pipeline with Automated Biological Entity Detection and Validation Service

Xu¹,

Gupta²,

Jaiswal

et al. 2019

Data and Information Management

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With the increasing amount of digital journal submissions, there is a need to deploy new scalable computational methods to improve information accessibilities. One common task is to identify useful information and named entity from text documents such as journal article submission. However, there are many technical challenges to limit applicability of the general methods and lack of general tools. In this paper, we present domain informational vocabulary extraction (DIVE) project, which aims to enrich digital publications through detection of entity and key informational words and by adding additional annotations. In a first of its kind to our knowledge, our system engages authors of the peer-reviewed articles and the journal publishers by integrating DIVE implementation in the manuscript proofing and publication process. The system implements multiple strategies for biological entity detection, including using regular expression rules, ontology, and a keyword dictionary. These extracted entities are then stored in a database and made accessible through an interactive web application for curation and evaluation by authors. Through the web interface, the authors can make additional annotations and corrections to the current results. The updates can then be used to improve the entity detection in subsequent processed articles in the future. We describe our framework and deployment in details. In a pilot program, we have deployed the first phase of development as a service integrated with the journals Plant Physiology and The Plant cell published by the American Society of Plant Biologists (ASPB). We present usage statistics to date since its production on April 2018. We compare automated recognition results from DIVE with results from author curation and show the service achieved on average 80% recall and 70% precision per article. In contrast, an existing biological entity extraction tool, a biomedical named entity recognizer (ABNER), can only achieve 47% recall and return a much larger candidate set.

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Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing

Cited by 27 publications

References 22 publications

Untitled

Untitled

Protein Named Entity Identification Based on Probabilistic Features Derived from GENIA Corpus and Medical Text on the Web

Improving Publication Pipeline with Automated Biological Entity Detection and Validation Service

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