Extracting Conceptual Terms from Medical Documents

Automated biomedical concept recognition is important for biomedical document retrieval and text mining research. In this paper, we describe a two-step concept extraction technique for documents in biomedical domain. Step one includes noun phrase extraction, which can automatically extract noun phrases from medical documents. Extracted noun phrases are used as concept term candidates which become inputs of next step. Step two includes keyphrase extraction, which can automatically identify important topical terms from candidate terms. Experiments were conducted to evaluate results of both steps. The experiment results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying document conceptual terms

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

A Relation Extraction Approach for Clinical Decision Support

In this paper, we investigate how semantic relations between concepts extracted from medical documents can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high informative power that can be leveraged to improve the effectiveness of retrieval functionalities of clinical decision support systems. We present preliminary results and show how relations are able to provide a sizable increase of the precision for several topics, albeit having no impact on others. We then discuss some future directions to minimize the impact of negative results while maximizing the impact of good results.Comment: 4 pages, 1 figure, DTMBio-KMH 2018, in conjunction with ACM 27th Conference on Information and Knowledge Management (CIKM), October 22-26 2018, Lingotto, Turin, Ital

Terminology Extraction for and from Communications in Multi-disciplinary Domains

Terminology extraction generally refers to methods and systems for identifying term candidates in a uni-disciplinary and uni-lingual environment such as engineering, medical, physical and geological sciences, or administration, business and leisure. However, as human enterprises get more and more complex, it has become increasingly important for teams in one discipline to collaborate with others from not only a non-cognate discipline but also speaking a different language. Disaster mitigation and recovery, and conflict resolution are amongst the areas where there is a requirement to use standardised multilingual terminology for communication. This paper presents a feasibility study conducted to build terminology (and ontology) in the domain of disaster management and is part of the broader work conducted for the EU project Sland \ub4 ail (FP7 607691). We have evaluated CiCui (for Chinese name \ub4 \u8bcd\u8403, which translates to words gathered), a corpus-based text analytic system that combine frequency, collocation and linguistic analyses to extract candidates terminologies from corpora comprised of domain texts from diverse sources. CiCui was assessed against four terminology extraction systems and the initial results show that it has an above average precision in extracting terms

Improving Term Extraction with Terminological Resources

Studies of different term extractors on a corpus of the biomedical domain revealed decreasing performances when applied to highly technical texts. The difficulty or impossibility of customising them to new domains is an additional limitation. In this paper, we propose to use external terminologies to influence generic linguistic data in order to augment the quality of the extraction. The tool we implemented exploits testified terms at different steps of the process: chunking, parsing and extraction of term candidates. Experiments reported here show that, using this method, more term candidates can be acquired with a higher level of reliability. We further describe the extraction process involving endogenous disambiguation implemented in the term extractor YaTeA