| 1. |
- Bengoetxea, Kepa, et al.
(författare)
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On WordNet Semantic Classes and Dependency Parsing
- 2014
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Ingår i: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). ; , s. 649-655
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Konferensbidrag (refereegranskat)
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| 2. |
- Nivre, Joakim, 1962-, et al.
(författare)
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Improving Dependency Parsing with Semantic Classes
- 2011
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Ingår i: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. - : Association for Computational Linguistics. ; , s. 699-703
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Konferensbidrag (refereegranskat)
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| 3. |
- Pérez, Alicia, et al.
(författare)
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Semi-supervised medical entity recognition : A study on Spanish and Swedish clinical corpora
- 2017
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Ingår i: Journal of Biomedical Informatics. - : Elsevier BV. - 1532-0464 .- 1532-0480. ; 71, s. 16-30
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised methods to generate such representations. Methods: The significance of this work stands on its experimental layout. The experiments were carried out under the same conditions for both languages. Several classification approaches were explored: maximum probability, CRF, Perceptron and SVM. The classifiers were enhanced by means of ensembles of semantic spaces and ensembles of Brown trees. In order to mitigate sparsity of data, without a significant increase in the dimension of the decision space, we propose the use of clustered approaches of the hierarchical Brown clustering represented by trees and vector quantization for each semantic space. Results: The results showed that the semi-supervised approaches significantly improved standard supervised techniques for both languages. Moreover, clustering the semantic spaces contributed to the quality of the entity recognition while keeping the dimension of the feature-space two orders of magnitude lower than when directly using the semantic spaces. Conclusions: The contributions of this study are: (a) a set of thorough experiments that enable comparisons regarding the influence of different types of features on different classifiers, exploring two languages other than English; and (b) the use of ensembles of clusters of Brown trees and semantic spaces on EHRs to tackle the problem of scarcity of available annotated data.
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| 4. |
- Seddah, Djamé, et al.
(författare)
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Overview of the SPMRL 2013 Shared Task : A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
- 2013
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Ingår i: Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically Rich Languages. - : Association for Computational Linguistics. - 9781937284978 ; , s. 146-182
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios.
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| 5. |
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| 6. |
- Weegar, Rebecka, et al.
(författare)
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Ensembles for clinical entity extraction
- 2018
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Ingår i: Revista de Procesamiento de Lenguaje Natural (SEPLN). - 1135-5948 .- 1989-7553. ; :60, s. 13-20
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Tidskriftsartikel (refereegranskat)abstract
- Health records are a valuable source of clinical knowledge and Natural Language Processing techniques have previously been applied to the text in health records for a number of applications. Often, a first step in clinical text processing is clinical entity recognition; identifying, for example, drugs, disorders, and body parts in clinical text. However, most of this work has focused on records in English. Therefore, this work aims to improve clinical entity recognition for languages other than English by comparing the same methods on two different languages, specifically by employing ensemble methods. Models were created for Spanish and Swedish health records using SVM, Perceptron, and CRF and four different feature sets, including unsupervised features. Finally, the models were combined in ensembles. Weighted voting was applied according to the models individual F-scores. In conclusion, the ensembles improved the overall performance for Spanish and the precision for Swedish.
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| 7. |
- Weegar, Rebecka, et al.
(författare)
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The impact of simple feature engineering in multilingual medical NER
- 2016
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Ingår i: Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP).
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Konferensbidrag (refereegranskat)abstract
- The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER. Sometimes papers using scientifically sound techniques present raw baselines that could be improved adding simple and cheap features. This work focuses on entity recognition for the clinical domain for three languages: English, Swedish and Spanish. The task is tackled using simple features, starting from the window size, capitalization, prefixes, and moving to POS and semantic tags. This work demonstrates that a simple initial step of feature engineering can improve the baseline results significantly. Hence, the contributions of this paper are: first, a short list of guidelines well supported with experimental results on three languages and, second, a detailed description of the relevance of these features for medical NER.
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