7,648 research outputs found
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
Using compression to identify acronyms in text
Text mining is about looking for patterns in natural language text, and may
be defined as the process of analyzing text to extract information from it for
particular purposes. In previous work, we claimed that compression is a key
technology for text mining, and backed this up with a study that showed how
particular kinds of lexical tokens---names, dates, locations, etc.---can be
identified and located in running text, using compression models to provide the
leverage necessary to distinguish different token types (Witten et al., 1999)Comment: 10 pages. A short form published in DCC200
A lexicon based approach to classification of ICD10 codes. IMS unipd at CLEF eHealth task 1
International audienc
Adaptive text mining: Inferring structure from sequences
Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies the way in which the adaptive techniques used in text compression can be applied to text mining. It develops several examples: extraction of hierarchical phrase structures from text, identification of keyphrases in documents, locating proper names and quantities of interest in a piece of text, text categorization, word segmentation, acronym extraction, and structure recognition. We conclude that compression forms a sound unifying principle that allows many text mining problems to be tacked adaptively
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
An iterative approach for lexicon characterization in juridical context
In the juridical context, knowledge management applications have a central role. In order to improve the effectiveness of document management procedures, techniques for automatic comprehension of textual content are required. In this work, a methodology for semi-automatic derivation of knowledge from document collections is proposed. In order to extract relevant information from document text, a process integrating both statistical and lexical approaches is applied. Moreover, we propose a system for the evaluation of the extracted peculiar lexicon quality. The system is used for the processing of heterogeneous documents corpus issued by Italyâs juridical domain
NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation
Biomedical researchers use ontologies to annotate their data with ontology
terms, enabling better data integration and interoperability. However, the
number, variety and complexity of current biomedical ontologies make it
cumbersome for researchers to determine which ones to reuse for their specific
needs. To overcome this problem, in 2010 the National Center for Biomedical
Ontology (NCBO) released the Ontology Recommender, which is a service that
receives a biomedical text corpus or a list of keywords and suggests ontologies
appropriate for referencing the indicated terms. We developed a new version of
the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new
recommendation approach that evaluates the relevance of an ontology to
biomedical text data according to four criteria: (1) the extent to which the
ontology covers the input data; (2) the acceptance of the ontology in the
biomedical community; (3) the level of detail of the ontology classes that
cover the input data; and (4) the specialization of the ontology to the domain
of the input data. Our evaluation shows that the enhanced recommender provides
higher quality suggestions than the original approach, providing better
coverage of the input data, more detailed information about their concepts,
increased specialization for the domain of the input data, and greater
acceptance and use in the community. In addition, it provides users with more
explanatory information, along with suggestions of not only individual
ontologies but also groups of ontologies. It also can be customized to fit the
needs of different scenarios. Ontology Recommender 2.0 combines the strengths
of its predecessor with a range of adjustments and new features that improve
its reliability and usefulness. Ontology Recommender 2.0 recommends over 500
biomedical ontologies from the NCBO BioPortal platform, where it is openly
available.Comment: 29 pages, 8 figures, 11 table
Natural Language Query in the Biochemistry and Molecular Biology Domains Based on Cognition Search™
Motivation: With the tremendous growth in scientific literature, it is necessary to improve upon the standard pattern matching style of the available search engines. Semantic NLP may be the solution to this problem. Cognition Search (CSIR) is a natural language technology. It is best used by asking a simple question that might be answered in textual data being queried, such as MEDLINE. CSIR has a large English dictionary and semantic database. Cognition’s semantic map enables the search process to be based on meaning rather than statistical word pattern matching and, therefore, returns more complete and relevant results. The Cognition Search engine uses downward reasoning and synonymy which also improves recall. It improves precision through phrase parsing and word sense disambiguation.
Result: Here we have carried out several projects to "teach" the CSIR lexicon medical, biochemical and molecular biological language and acronyms from curated web-based free sources. Vocabulary from the Alliance for Cell Signaling (AfCS), the Human Genome Nomenclature Consortium (HGNC), the United Medical Language System (UMLS) Meta-thesaurus, and The International Union of Pure and Applied Chemistry (IUPAC) was introduced into the CSIR dictionary and curated. The resulting system was used to interpret MEDLINE abstracts. Meaning-based search of MEDLINE abstracts yields high precision (estimated at >90%), and high recall (estimated at >90%), where synonym information has been encoded. The present implementation can be found at http://MEDLINE.cognition.com. 

Acronyms as an integral part of multiâword term recognition - A token of appreciation
Term conflation is the process of linking together different variants of the same term. In automatic term recognition approaches, all term variants should be aggregated into a single normalized term representative, which is associated with a single domainâspecific concept as a latent variable. In a previous study, we described FlexiTerm, an unsupervised method for recognition of multiâword terms from a domainâspecific corpus. It uses a range of methods to normalize three types of term variation â orthographic, morphological and syntactic variation. Acronyms, which represent a highly productive type of term variation, were not supported. In this study, we describe how the functionality of FlexiTerm has been extended to recognize acronyms and incorporate them into the term conflation process. The main contribution of this study is not acronym recognition per se, but rather its integration with other types of term variation into the term conflation process. We evaluated the effects of term conflation in the context of information retrieval as one of its most prominent applications. On average, relative recall increased by 32 percent points, whereas index compression factor increased by 7 percent points. Therefore, evidence suggests that integration of acronyms provides nonâtrivial improvement of term conflation
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