24,958 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
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A lightweight, pattern-based approach to identification and formalisation of TimeML expressions in clinical narratives
General Architecture for Text Engineering (GATE) components for identifying clinical events and temporal expressions are developed and evaluated against a corpus of 120 discharge summaries
A Supervised Learning Approach to Acronym Identification
This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a
supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features
used by our supervised learning system, and our experiments with various learning schemes
On the Effect of Semantically Enriched Context Models on Software Modularization
Many of the existing approaches for program comprehension rely on the
linguistic information found in source code, such as identifier names and
comments. Semantic clustering is one such technique for modularization of the
system that relies on the informal semantics of the program, encoded in the
vocabulary used in the source code. Treating the source code as a collection of
tokens loses the semantic information embedded within the identifiers. We try
to overcome this problem by introducing context models for source code
identifiers to obtain a semantic kernel, which can be used for both deriving
the topics that run through the system as well as their clustering. In the
first model, we abstract an identifier to its type representation and build on
this notion of context to construct contextual vector representation of the
source code. The second notion of context is defined based on the flow of data
between identifiers to represent a module as a dependency graph where the nodes
correspond to identifiers and the edges represent the data dependencies between
pairs of identifiers. We have applied our approach to 10 medium-sized open
source Java projects, and show that by introducing contexts for identifiers,
the quality of the modularization of the software systems is improved. Both of
the context models give results that are superior to the plain vector
representation of documents. In some cases, the authoritativeness of
decompositions is improved by 67%. Furthermore, a more detailed evaluation of
our approach on JEdit, an open source editor, demonstrates that inferred topics
through performing topic analysis on the contextual representations are more
meaningful compared to the plain representation of the documents. The proposed
approach in introducing a context model for source code identifiers paves the
way for building tools that support developers in program comprehension tasks
such as application and domain concept location, software modularization and
topic analysis
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Lexical patterns, features and knowledge resources for coreference resolution in clinical notes
Generation of entity coreference chains provides a means to extract linked narrative events from clinical notes, but despite being a well-researched topic in natural language processing, general- purpose coreference tools perform poorly on clinical texts. This paper presents a knowledge-centric and pattern-based approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA). In addition, a method for generating coreference chains using progressively pruned linked lists is demonstrated that reduces the search space and facilitates evaluation by a number of metrics. Independent evaluation results show an F-measure for each corpus of 79.2% and 87.5%, respectively, which offers performance at least as good as human annotators, greatly increased performance over general- purpose tools, and improvement on previously reported clinical coreference systems. The system uses a number of open-source components that are available to download
Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus
The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning
Information extraction from medication leaflets
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
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