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Learning multiple distributed prototypes of semantic categories for named entity recognition
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2015 (English)In: International Journal of Data Mining and Bioinformatics, ISSN 1748-5681, Vol. 13, no 4, 395-411 p.Article in journal (Refereed) Published
Abstract [en]

The scarcity of large labelled datasets comprising clinical text that can be exploited within the paradigm of supervised machine learning creates barriers for the secondary use of data from electronic health records. It is therefore important to develop capabilities to leverage the large amounts of unlabelled data that, indeed, tend to be readily available. One technique utilises distributional semantics to create word representations in a wholly unsupervised manner and uses existing training data to learn prototypical representations of predefined semantic categories. Features describing whether a given word belongs to a certain category are then provided to the learning algorithm. It has been shown that using multiple distributional semantic models, each employing a different word order strategy, can lead to enhanced predictive performance. Here, another hyperparameter is also varied – the size of the context window – and an experimental investigation shows that this leads to further performance gains.

Place, publisher, year, edition, pages
2015. Vol. 13, no 4, 395-411 p.
Keyword [en]
distributional semantics, semantic space ensembles, random indexing, named entity recognition, electronic health records, de-identification
National Category
Computer Science Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-122461DOI: 10.1504/IJDMB.2015.072766ISI: 000366135400005OAI: oai:DiVA.org:su-122461DiVA: diva2:866458
Projects
High-Performance Data Mining for Drug Effect Detection
Funder
Swedish Foundation for Strategic Research , IIS11-0053
Available from: 2015-11-02 Created: 2015-11-02 Last updated: 2016-01-04Bibliographically approved
In thesis
1. Ensembles of Semantic Spaces: On Combining Models of Distributional Semantics with Applications in Healthcare
Open this publication in new window or tab >>Ensembles of Semantic Spaces: On Combining Models of Distributional Semantics with Applications in Healthcare
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i.e., human annotations are not required. The ability to obtain inexpensive word representations in this manner helps to alleviate the bottleneck of fully supervised approaches to natural language processing, especially since models of distributional semantics are data-driven and hence agnostic to both language and domain.

All that is required to obtain distributed word representations is a sizeable corpus; however, the composition of the semantic space is not only affected by the underlying data but also by certain model hyperparameters. While these can be optimized for a specific downstream task, there are currently limitations to the extent the many aspects of semantics can be captured in a single model. This dissertation investigates the possibility of capturing multiple aspects of lexical semantics by adopting the ensemble methodology within a distributional semantic framework to create ensembles of semantic spaces. To that end, various strategies for creating the constituent semantic spaces, as well as for combining them, are explored in a number of studies.

The notion of semantic space ensembles is generalizable across languages and domains; however, the use of unsupervised methods is particularly valuable in low-resource settings, in particular when annotated corpora are scarce, as in the domain of Swedish healthcare. The semantic space ensembles are here empirically evaluated for tasks that have promising applications in healthcare. It is shown that semantic space ensembles – created by exploiting various corpora and data types, as well as by adjusting model hyperparameters such as the size of the context window and the strategy for handling word order within the context window – are able to outperform the use of any single constituent model on a range of tasks. The semantic space ensembles are used both directly for k-nearest neighbors retrieval and for semi-supervised machine learning. Applying semantic space ensembles to important medical problems facilitates the secondary use of healthcare data, which, despite its abundance and transformative potential, is grossly underutilized.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. 95 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-021
Keyword
natural language processing, machine learning, distributional semantics, ensemble learning, semantic space ensembles, medical informatics, electronic health records
National Category
Computer Science Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-122465 (URN)978-91-7649-302-1 (ISBN)
Public defence
2015-12-17, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Projects
High-Performance Data Mining for Drug Effect Detection
Funder
Swedish Foundation for Strategic Research , IIS11-0053
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4 and 5: Unpublished conference papers.

Available from: 2015-11-25 Created: 2015-11-02 Last updated: 2015-11-13Bibliographically approved

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