The Discriminative Word Lexicon (DWL) is a maximum-entropy model that predicts the target word probability given the source sentence words. We present two ways to extend a DWL to improve its ability to model the word ...
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The proceedings contain 13 papers. The topics discussed include: structured databases of named entities from Bayesian nonparametrics;unsupervised cross-lingual lexical substitution;reducing the size of the representat...
ISBN:
(纸本)1937284131
The proceedings contain 13 papers. The topics discussed include: structured databases of named entities from Bayesian nonparametrics;unsupervised cross-lingual lexical substitution;reducing the size of the representation for the uDOP-estimate;evaluating unsupervised learning for naturallanguageprocessing tasks;unsupervised language-independent name translation mining from Wikipedia infoboxes;twitter polarity classification with label propagation over lexical links and the follower graph;unsupervised concept annotation using latent Dirichlet allocation and segmental methods;and unsupervised alignment for segmental-basedlanguage understanding.
We learn graph-based similarity measures for the task of extracting word synonyms from a corpus of parsed text. A constrained graph walk variant that has been successfully applied in the past in similar settings is sh...
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Background: Named entity recognition (NER) is an important task in clinical naturallanguageprocessing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in cl...
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Background: Named entity recognition (NER) is an important task in clinical naturallanguageprocessing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in clinical text. Algorithms and features are two important factors that largely affect the performance of ML-based NER systems. Conditional Random Fields (CRFs), a sequential labelling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to clinical NER tasks. For features, syntactic and semantic information of context words has often been used in clinical NER systems. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, and word representation features, which contain word-level back-off information over large unlabelled corpus by unsupervised algorithms, have not been extensively investigated for clinical text processing. Therefore, the primary goal of this study is to evaluate the use of SSVMs and word representation features in clinical NER tasks. methods: In this study, we developed SSVMs-based NER systems to recognize clinical entities in hospital discharge summaries, using the data set from the concept extration task in the 2010 i2b2 NLP challenge. We compared the performance of CRFs and SSVMs-based NER classifiers with the same feature sets. Furthermore, we extracted two different types of word representation features (clustering-based representation features and distributional representation features) and integrated them with the SSVMs-based clinical NER system. We then reported the performance of SSVM-based NER systems with different types of word representation features. Results and discussion: Using the same training (N = 27,837) and test (N = 45,009) sets in the challenge, our evaluation showed that the SSVMs-based NER systems achieved better performance than the CRFs-based sy
To be able to answer the question What causes tumors to shrink?, one would require a large cause-effect relation repository. Many efforts have been payed on is-a and part-of relation leaning, however few have focused ...
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The proceedings contain 30 papers. The topics discussed include: graph-based alignment of narratives for automated neurological assessment;bootstrapping biomedical ontologies for scientific text using nell;semantic di...
ISBN:
(纸本)9781937284206
The proceedings contain 30 papers. The topics discussed include: graph-based alignment of narratives for automated neurological assessment;bootstrapping biomedical ontologies for scientific text using nell;semantic distance and terminology structuring methods for the detection of semantically close terms;temporal classification of medical events;analyzing patient records to establish if and when a patient suffered from a medical condition;alignment-HMM-based extraction of abbreviations from biomedical text;medical diagnosis lost in translation – analysis of uncertainty and negation expressions in English and Swedish clinical texts;and a hybrid stepwise approach for de-identifying person names in clinical documents.
The proceedings contain 8 papers. The topics discussed include: evaluating answers to reading comprehension questions in context: results for German and the role of information structure;towards a probabilistic model ...
ISBN:
(纸本)1937284158
The proceedings contain 8 papers. The topics discussed include: evaluating answers to reading comprehension questions in context: results for German and the role of information structure;towards a probabilistic model for lexical entailment;classification-based contextual preferences;is it worth submitting this run? assess your RTE system with a good sparring partner;diversity-aware evaluation for paraphrase patterns;representing and resolving ambiguities in ontology-based question answering;strings over intervals;and discovering commonsense entailment rules implicit in sentences.
The proceedings contain 17 papers. The topics discussed include: intersection for weighted formalisms;modularization of regular growth automata;finite-state representations embodying temporal relation;supervised and s...
The proceedings contain 17 papers. The topics discussed include: intersection for weighted formalisms;modularization of regular growth automata;finite-state representations embodying temporal relation;supervised and semi-supervised sequence learning for recognition of requisite part and effectuation part in law sentences;compiling simple context restrictions with nondeterministic automata;constraint grammar parsing with left and right sequential finite transducers;e-dictionaries and finite-state automata for the recognition of named entities;a practical algorithm for intersecting weighted context-free grammars with finite-state automata;open source WFST tools for LVCSR cascade development;intersection of multitape transducers vs. cascade of binary transducers: the example of Egyptian hieroglyphs transliteration;and a note on sequential rule-based POS tagging.
An increasing need for collaboration and resources sharing in the naturallanguageprocessing (NLP) research and development community motivates efforts to create and share a common data model and a common terminology...
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An increasing need for collaboration and resources sharing in the naturallanguageprocessing (NLP) research and development community motivates efforts to create and share a common data model and a common terminology for all information annotated and extracted from clinical text. We have combined two existing standards: the HL7 Clinical Document Architecture (CDA), and the ISO graph Annotation Format (GrAF;in development), to develop such a data model entitled "CDA+GrAF". We experimented with several methods to combine these existing standards, and eventually selected a method wrapping separate CDA and GrAF parts in a common standoff annotation (i.e., separate from the annotated text) XML document. Two use cases, clinical document sections, and the 2010 i2b2/VA NLP Challenge (i.e., problems, tests, and treatments, with their assertions and relations), were used to create examples of such standoff annotation documents, and were successfully validated with the XML schemata provided with both standards. We developed a tool to automatically translate annotation documents from the 2010 i2b2/VA NLP Challenge format to GrAF, and automatically generated 50 annotation documents using this tool, all successfully validated. Finally, we adapted the XSL stylesheet provided with HL7 CDA to allow viewing annotation XML documents in a web browser, and plan to adapt existing tools for translating annotation documents between CDA+GrAF and the UIMA and GATE frameworks. This common data model may ease directly comparing NLP tools and applications, combining their output, transforming and "translating" annotations between different NLP applications, and eventually "plug-and-play" of different modules in NLP applications. (c) 2011 Elsevier Inc. All rights reserved.
This paper presents DAGGER, a toolkit for finite-state automata that operate on directed acyclic graphs (dags). The work is based on a model introduced by (Kamimura and Slutzki, 1981;Kamimura and Slutzki, 1982), with ...
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