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We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents.
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We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures.
Such methods for inducing models of distributional semantics, in an unsupervised and language independent fashion, have shown to perform well at a range of NLP tasks, including more generic IR [ 43, 6- 47] and clinical IR for health records, see participants of the TREC Medical Records track [ 35, 36].
This approach combines term ranking, localised language analysis, unsupervised topic clustering and multilingual sentiment analysis to extract prominent topics through analysis of Twitter's tweets from a period of time.
We then employ an ASR back-end that uses neural networks for acoustic and language modeling and unsupervised environmental adaptation.
Language-independent and unsupervised training-based approaches have also been considered within this program aiming at building a system for an unknown language [82].
However, in a different setting, these links were also used to collect data for the unsupervised adaptation of the ASR system language model [35].
An unsupervised machine learning technique extensively used in language recognition, GI reveals underlying structure of symbolic sequential data.
All unsupervised methods are implemented in the R language, and SIRENE in MATLAB.
We conducted the unsupervised hierarchical clustering analysis using R programming language (version 2.15.1) to evaluate batch effects among 92 replicated sequencing experiments.
These results are comparable to state of the art unsupervised techniques of sentiment analysis for the Spanish language [23].
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