Named entity recognition with multiple segment representations

Cho, Han-Cheol; Okazaki, Naoaki; Misawa, Miwa; Tsujii, Jun’ichi

doi:10.1016/j.ipm.2013.03.002

Cited by 41 publications

(16 citation statements)

References 10 publications

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“…In machine learning-based NER systems, the NER problem is usually converted into a classification problem by representing entities using specific tags. There are various representations for named entities [ 27 ] which are also suitable for chemical entities. In our study, we used BIO tags, a typical representation for named entities, to represent chemical entities, where "B", "I" and "O" denote the beginning, inside and outside of an entity respectively.…”

Section: Methodsmentioning

confidence: 99%

A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature

et al. 2015

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BackgroundChemical compounds and drugs (together called chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of chemical entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of chemical entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task.MethodsThe 2013 CHEMDNER challenge organizers provided a manually annotated 10,000 UTF8-encoded PubMed abstracts according to a predefined annotation guideline: a training set of 3,500 abstracts, a development set of 3,500 abstracts and a test set of 3,000 abstracts. We developed machine learning-based systems, based on conditional random fields (CRF) and structured support vector machines (SSVM) respectively, for the CEM task for this data set. The effects of three types of word representation (WR) features, generated by Brown clustering, random indexing and skip-gram, on both two machine learning-based systems were also investigated. The performance of our system was evaluated on the test set using scripts provided by the CHEMDNER challenge organizers. Primary evaluation measures were micro Precision, Recall, and F-measure.ResultsOur best system was among the top ranked systems with an official micro F-measure of 85.05%. Fixing a bug caused by inconsistent features marginally improved the performance (micro F-measure of 85.20%) of the system.ConclusionsThe SSVM-based CEM systems outperformed the CRF-based CEM systems when using the same features. Each type of the WR feature was beneficial to the CEM task. Both the CRF-based and SSVM-based systems using the all three types of WR features showed better performance than the systems using only one type of the WR feature.

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Section: Methodsmentioning

confidence: 99%

A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature

et al. 2015

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“…Named Entity Recognition enables the localization and categorization of "important and proper nouns in a text" [27], helping the reviewer to focus on important concepts that characterize the text. According to Reference [28], "this is an important task because its performance directly affects the quality of many succeeding NLP applications such as information extraction". Its application recently gained popularity for processing semi-structured knowledge bases regarding entity disambiguation/mapping [29][30][31] and extracting/retrieving information [32] or for analyzing content generated on social media [33][34][35].…”

Section: Natural Language Processing Approachesmentioning

confidence: 99%

Hit or Miss? Evaluating the Potential of a Research Niche: A Case Study in the Field of Virtual Quality Management

et al. 2019

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When knowledge is developed fast, as it is the case so often nowadays, one of the main difficulties in initiating new research in any field is to identify the domain's specific state-of-the-art and trends. In this context, to evaluate the potential of a research niche by assisting the literature review process and to add a new and modern large-scale and automated dimension to it, the paper proposes a methodology that uses "Latent Semantic Analysis" (LSA) for identifying trends, focused within the knowledge space created at the intersection of three sustainability-related methodologies/concepts: "virtual Quality Management" (vQM), "Industry 4.0", and "Product Life-Cycle" (PLC). The LSA was applied to a significant number of scientific papers published around these concepts to generate ontology charts that describe the knowledge structure of each by the frequency, position, and causal relation of associated notions. These notions are combined for defining the common high-density knowledge zone from where new technological solutions are expected to emerge throughout the PLC. The authors propose the concept of the knowledge space, which is characterized through specific descriptors with their own evaluation scales, obtained by processing the emerging information as identified by a combination of classic and innovative techniques. The results are validated through an investigation that surveys a relevant number of general managers, specialists, and consultants in the field of quality in the automotive sector from Romania. This practical demonstration follows each step of the theoretical approach and yields results that prove the capability of the method to contribute to the understanding and elucidation of the scientific area to which it is applied. Once validated, the method could be transferred to fields with similar characteristics. Even if their creators endowed them with a clear meaning at an incipient stage, when they become more popular in an emerging area, these concepts are quickly surrounded by a large amount of new knowledge that is developed with an amazing speed, enriching and enlarging their initial sphere.The "virtual Quality Management" (vQM) concept could be a significant example for the circumstances described previously. It is born through a semantic operation, joining two established and mature concepts: "virtual" and "QM", thus it is representative for an area which is in a period of high dynamic development and of interest for companies preoccupied with sustainability from the perspective of operations management and organizational culture.In this context in which the amount of information relating to new concepts quickly reaches unmanageable levels, regardless of the field, solutions that can analyze extended documentation with the purpose of disambiguating information and capturing the essentials, thus creating knowledge, become the focus of attention and gain in importance. Traditional solutions for that purpose lay in the literature review process, trying to collect, select, filter, and struc...

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“…The recognition of software name is a typical NER problem in the area of NLP, which could be viewed as a sequential labeling problem. 10 Each token in the sentence can be represented as one of "B,""I" and "O" labels. "B" means the beginning of a software name, "I" represents the other words in the software name and "O" represents words that are not inside software names.…”

Section: Software Entity Recognitionmentioning

confidence: 99%

Recognizing software names in biomedical literature using machine learning

Wei¹,

Zhang²,

Amith

et al. 2019

Health Informatics J

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Software tools now are essential to research and applications in the biomedical domain. However, existing software repositories are mainly built using manual curation, which is time-consuming and unscalable. This study took the initiative to manually annotate software names in 1,120 MEDLINE abstracts and titles and used this corpus to develop and evaluate machine learning–based named entity recognition systems for biomedical software. Specifically, two strategies were proposed for feature engineering: (1) domain knowledge features and (2) unsupervised word representation features of clustered and binarized word embeddings. Our best system achieved an F-measure of 91.79% for recognizing software from titles and an F-measure of 86.35% for recognizing software from both titles and abstracts using inexact matching criteria. We then created a biomedical software catalog with 19,557 entries using the developed system. This study demonstrates the feasibility of using natural language processing methods to automatically build a high-quality software index from biomedical literature.

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Named entity recognition with multiple segment representations

Cited by 41 publications

References 10 publications

A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature

A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature

Hit or Miss? Evaluating the Potential of a Research Niche: A Case Study in the Field of Virtual Quality Management

Recognizing software names in biomedical literature using machine learning

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