scholarly journals Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

2012 ◽  
Vol 35 (1) ◽  
pp. 87-109 ◽  
Author(s):  
César de Pablo-Sánchez ◽  
Isabel Segura-Bedmar ◽  
Paloma Martínez ◽  
Ana Iglesias-Maqueda
Keyword(s):  
Text Mining ◽  
Named Entities ◽  
Linguistic Patterns ◽  
Multilingual Text

A multilingual text mining approach to web cross-lingual text retrieval

2004 ◽  
Vol 17 (5-6) ◽  
pp. 219-227 ◽  
Author(s):  
Rowena Chau ◽  
Chung-Hsing Yeh
Keyword(s):  
Text Mining ◽  
Text Retrieval ◽  
Multilingual Text ◽  
Cross Lingual

Multilingual Text Mining

2011 ◽  
pp. 1380-1385 ◽  
Author(s):  
Peter A. Chew
Keyword(s):  
Text Mining ◽  
Search Engines ◽  
Text Processing ◽  
The European Union ◽  
End User ◽  
The World ◽  
Recent Developments ◽  
English Speaking ◽  
Multilingual Text ◽  
Multiple Languages

The principles of text mining are fundamental to technology in everyday use. The world wide web (WWW) has in many senses driven research in text mining, and with the growth of the WWW, applications of text mining (like search engines) have by now become commonplace. In a way that was not true even less than a decade ago, it is taken for granted that the ‘needle in the haystack’ can quickly be found among large volumes of text. In most cases, however, users still expect search engines to return results in the same language as that of the query, perhaps the language best understood by the user, or the language in which text is most likely to be available. The distribution of languages on the WWW does not match the distribution of languages spoken in general by the world’s population. For example, while English is spoken by under 10% of the world’s population (Gordon 2005), it is still predominant on the WWW, accounting for perhaps two-thirds of documents. There are variety of possible reasons for this disparity, including technological inequities between different parts of the world and the fact that the WWW had its genesis in an English-speaking country. Whatever the cause for the dominance of English, the fact that two-thirds of the WWW is in one language is, in all likelihood, a major reason that the concept of multilingual text mining is still relatively new. Until recently, there simply has not been a significant and widespread need for multilingual text mining. A number of recent developments have begun to change the situation, however. Perhaps these developments can be grouped under the general rubric of ‘globalization’. They include the increasing adoption, use, and popularization of the WWW in non-Englishspeaking societies; the trend towards political integration of diverse linguistic communities (highly evident, for example, in the European Union); and a growing interest in understanding social, technological and political developments in other parts of the world. All these developments contribute to a greater demand for multilingual text processing – essentially, methods for handling, managing, and comparing documents in multiple languages, some of which may not even be known to the end user.

A RE-EVALUATION OF BIOMEDICAL NAMED ENTITY–TERM RELATIONS

2010 ◽  
Vol 08 (05) ◽  
pp. 917-928 ◽  
Author(s):  
TOMOKO OHTA ◽  
SAMPO PYYSALO ◽  
JIN-DONG KIM ◽  
JUN'ICHI TSUJII
Keyword(s):  
Text Mining ◽  
Biomedical Text Mining ◽  
Corpus Annotation ◽  
Named Entities ◽  
Detailed Model ◽  
Named Entity ◽  
Textual Data ◽  
Recent Developments ◽  
Relevant Domain ◽  
Domain Information

Text mining can support the interpretation of the enormous quantity of textual data produced in biomedical field. Recent developments in biomedical text mining include advances in the reliability of the recognition of named entities (NEs) such as specific genes and proteins, as well as movement toward richer representations of the associations of NEs. We argue that this shift in representation should be accompanied by the adoption of a more detailed model of the relations holding between NEs and other relevant domain terms. As a step toward this goal, we study NE–term relations with the aim of defining a detailed, broadly applicable set of relation types based on accepted domain standard concepts for use in corpus annotation and domain information extraction approaches.

scholarly journals Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy

Database ◽  
2017 ◽  
Vol 2017 ◽  
Author(s):  
Nai-Wen Chang ◽  
Hong-Jie Dai ◽  
Yung-Yu Shih ◽  
Chi-Yang Wu ◽  
Mira Anne C Dela Rosa ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Text Mining ◽  
Literature Mining ◽  
Biomedical Text Mining ◽  
Biomedical Knowledge ◽  
Named Entities ◽  
Biomarker Identification ◽  
Wide Range ◽  
Common Causes ◽  
Journal Impact

Abstract Hepatocellular carcinoma (HCC), one of the most common causes of cancer-related deaths, carries a 5-year survival rate of 18%, underscoring the need for robust biomarkers. In spite of the increased availability of HCC related literatures, many of the promising biomarkers reported have not been validated for clinical use. To narrow down the wide range of possible biomarkers for further clinical validation, bioinformaticians need to sort them out using information provided in published works. Biomedical text mining is an automated way to obtain information of interest within the massive collection of biomedical knowledge, thus enabling extraction of data for biomarkers associated with certain diseases. This method can significantly reduce both the time and effort spent on studying important maladies such as liver diseases. Herein, we report a text mining-aided curation pipeline to identify potential biomarkers for liver cancer. The curation pipeline integrates PubMed E-Utilities to collect abstracts from PubMed and recognize several types of named entities by machine learning-based and pattern-based methods. Genes/proteins from evidential sentences were classified as candidate biomarkers using a convolutional neural network. Lastly, extracted biomarkers were ranked depending on several criteria, such as the frequency of keywords and articles and the journal impact factor, and then integrated into a meaningful list for bioinformaticians. Based on the developed pipeline, we constructed MarkerHub, which contains 2128 candidate biomarkers extracted from PubMed publications from 2008 to 2017. Database URL: http://markerhub.iis.sinica.edu.tw

scholarly journals Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822198939
Author(s):  
Euisung Jung ◽  
Hemant Jain ◽  
Atish P Sinha ◽  
Carmelo Gaudioso
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Text Mining ◽  
Language Processing ◽  
Snomed Ct ◽  
Lexical Resources ◽  
Named Entities ◽  
Domain Experts ◽  
Trial Subject

A natural language processing (NLP) application requires sophisticated lexical resources to support its processing goals. Different solutions, such as dictionary lookup and MetaMap, have been proposed in the healthcare informatics literature to identify disease terms with more than one word (multi-gram disease named entities). Although a lot of work has been done in the identification of protein- and gene-named entities in the biomedical field, not much research has been done on the recognition and resolution of terminologies in the clinical trial subject eligibility analysis. In this study, we develop a specialized lexicon for improving NLP and text mining analysis in the breast cancer domain, and evaluate it by comparing it with the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). We use a hybrid methodology, which combines the knowledge of domain experts, terms from multiple online dictionaries, and the mining of text from sample clinical trials. Use of our methodology introduces 4243 unique lexicon items, which increase bigram entity match by 38.6% and trigram entity match by 41%. Our lexicon, which adds a significant number of new terms, is very useful for matching patients to clinical trials automatically based on eligibility matching. Beyond clinical trial matching, the specialized lexicon developed in this study could serve as a foundation for future healthcare text mining applications.

scholarly journals Impresso Inspect and Compare. Visual Comparison of Semantically Enriched Historical Newspaper Articles

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 348 ◽  
Author(s):  
Marten Düring ◽  
Roman Kalyakin ◽  
Estelle Bunout ◽  
Daniele Guido
Keyword(s):  
Text Mining ◽  
Interface Design ◽  
State Of The Art ◽  
Critical Assessment ◽  
Data Driven ◽  
Distant Reading ◽  
Named Entities ◽  
Document Collections ◽  
Search Queries ◽  
Current State

The automated enrichment of mass-digitised document collections using techniques such as text mining is becoming increasingly popular. Enriched collections offer new opportunities for interface design to allow data-driven and visualisation-based search, exploration and interpretation. Most such interfaces integrate close and distant reading and represent semantic, spatial, social or temporal relations, but often lack contrastive views. Inspect and Compare (I&C) contributes to the current state of the art in interface design for historical newspapers with highly versatile side-by-side comparisons of query results and curated article sets based on metadata and semantic enrichments. I&C takes search queries and pre-curated article sets as inputs and allows comparisons based on the distributions of newspaper titles, publication dates and automatically generated enrichments, such as language, article types, topics and named entities. Contrastive views of such data reveal patterns, help humanities scholars to improve search strategies and to facilitate a critical assessment of the overall data quality. I&C is part of the impresso interface for the exploration of digitised and semantically enriched historical newspapers.

scholarly journals Topic Categorisation of Statements in Suicide Notes with Integrated Rules and Machine Learning

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8978 ◽  
Author(s):  
Aleksandar Kovačević ◽  
Azad Dehghan ◽  
John A. Keane ◽  
Goran Nenadic
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Training Dataset ◽  
Learning Models ◽  
Named Entities ◽  
Suicide Notes ◽  
F Measure ◽  
Machine Learning Models ◽  
Testing Set

We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

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