Event Extraction from Biomedical Literature

The breadth and scope of the biomedical literature hinders a timely and thorough comprehension of its content. PubMed, the leading repository for biomedical literature, currently holds over 26 million records, and is growing at a rate of over 1.2 million records per year, with about 300 records added daily that mention `cancer' in the title or abstract. Natural language processing (NLP) can assist in accessing and interpreting this massive volume of literature, including its quality. NLP approaches to the automatic extraction of biomedical entities and relationships may assist the development of explanatory models that can comprehensively scan and summarize biomedical articles for end users. Users can also formulate structured queries against these entities, and their interactions, to mine the latest developments in related areas of interest. In this article, we explore the latest advances in automated event extraction methods in the biomedical domain, focusing primarily on tools participated in the Biomedical NLP (BioNLP) Shared Task (ST) competitions. We review the leading BioNLP methods, summarize their results, and their innovative contributions in this field.

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Events Automatic Extraction from Arabic Texts

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2016010103 ◽

2016 ◽

Vol 6 (1) ◽

pp. 36-51 ◽

Cited By ~ 2

Author(s):

Emna Hkiri ◽

Souheyl Mallat ◽

Mounir Zrigui

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Language Processing ◽

Question Answering ◽

Arabic Language ◽

Event Extraction ◽

Mining Machine ◽

Automatic Extraction ◽

Open Domain ◽

Ongoing Effort

The event extraction task consists in determining and classifying events within an open-domain text. It is very new for the Arabic language, whereas it attained its maturity for some languages such as English and French. Events extraction was also proved to help Natural Language Processing tasks such as Information Retrieval and Question Answering, text mining, machine translation etc… to obtain a higher performance. In this article, we present an ongoing effort to build a system for event extraction from Arabic texts using Gate platform and other tools.

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An Overview of Biomolecular Event Extraction from Scientific Documents

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/571381 ◽

2015 ◽

Vol 2015 ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Jorge A. Vanegas ◽

Sérgio Matos ◽

Fabio González ◽

José L. Oliveira

Keyword(s):

Natural Language ◽

Language Processing ◽

State Of The Art ◽

Biomedical Literature ◽

Event Extraction ◽

Automatic Extraction ◽

Biological Processes ◽

Scientific Texts ◽

Research Areas ◽

Current State

This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.

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Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

ACM SIGIR Forum ◽

10.1145/3458553.3458554 ◽

2019 ◽

Vol 53 (2) ◽

pp. 3-10

Author(s):

Muthu Kumar Chandrasekaran ◽

Philipp Mayr

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Research And Development ◽

Language Processing ◽

Digital Libraries ◽

State Of The Art ◽

Shared Task ◽

Processing Information ◽

Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

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Automatic Extraction and Classification of Patients’ Smoking Status from Free Text Using Natural Language Processing

Value in Health ◽

10.1016/j.jval.2016.09.158 ◽

2016 ◽

Vol 19 (7) ◽

pp. A373

Author(s):

A Caccamisi ◽

L Jörgensen ◽

H Dalianis ◽

M Rosenlund

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Smoking Status ◽

Free Text ◽

Automatic Extraction

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Automatic Extraction of Major Osteoporotic Fractures from Radiology Reports using Natural Language Processing

2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W) ◽

10.1109/ichi-w.2018.00021 ◽

2018 ◽

Author(s):

Yanshan Wang ◽

Saeed Mehrabi ◽

Sunghwan Sohn ◽

Elizabeth Atkinson ◽

Shreyasee Amin ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Osteoporotic Fractures ◽

Automatic Extraction ◽

Radiology Reports ◽

Major Osteoporotic Fractures

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Word Embedding for Semantically Relative Words: an Experimental Study

Modeling and Analysis of Information Systems ◽

10.18255/1818-1015-2018-6-726-733 ◽

2018 ◽

Vol 25 (6) ◽

pp. 726-733

Author(s):

Maria S. Karyaeva ◽

Pavel I. Braslavski ◽

Valery A. Sokolov

Keyword(s):

Experimental Study ◽

Natural Language Processing ◽

Language Processing ◽

Intelligent Systems ◽

Russian Language ◽

Word Embedding ◽

Semantic Relations ◽

Automatic Extraction ◽

Semantic Relationships ◽

The Russian Language

The ability to identify semantic relations between words has made a word2vec model widely used in NLP tasks. The idea of word2vec is based on a simple rule that a higher similarity can be reached if two words have a similar context. Each word can be represented as a vector, so the closest coordinates of vectors can be interpreted as similar words. It allows to establish semantic relations (synonymy, relations of hypernymy and hyponymy and other semantic relations) by applying an automatic extraction. The extraction of semantic relations by hand is considered as a time-consuming and biased task, requiring a large amount of time and some help of experts. Unfortunately, the word2vec model provides an associative list of words which does not consist of relative words only. In this paper, we show some additional criteria that may be applicable to solve this problem. Observations and experiments with well-known characteristics, such as word frequency, a position in an associative list, might be useful for improving results for the task of extraction of semantic relations for the Russian language by using word embedding. In the experiments, the word2vec model trained on the Flibusta and pairs from Wiktionary are used as examples with semantic relationships. Semantically related words are applicable to thesauri, ontologies and intelligent systems for natural language processing.

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