Sequential labeling addresses the classification of sequential data and is of increasing importance for the classification and segmentation of video data. The model traditionally used for sequential labeling is the hidden Markov model where the sequence of class labels to be predicted is encoded as a Markov chain. In recent years, hidden Markov models and other structural models have benefited ...

OBJECTIVE To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. DESIGN We used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. ...

The present study elaborates on the exploitation of both linguistic and acoustic feature modeling for anger classification. In terms of acoustic modeling we generate statistics from acoustic audio descriptors, e.g. pitch, loudness, spectral characteristics. Ranking our features we see that loudness and MFCC seems most promising for all databases. For the English database also pitch features are...

objective: the lack of adequate recognition of health importance of non-hiv reproductive health infections (rhis) in nigeria has led into this study, which was to determine clinical pathogens in non-hiv rhi in nigeria using a tertiary health facility as case study. materials and methods: a nine-year investigation was carried out between 1997 and 2005 on 4047 (n = 1626 males; n = 2421 females) p...

Available online 19 September 2012 JEL classification: C72 D72 D74

This paper describes a system to solve the joint learning of syntactic and semantic dependencies. An directed graphical model is put forward to integrate dependency relation classification and semantic role labeling. We present a bilayer directed graph to express probabilistic relationships between syntactic and semantic relations. Maximum Entropy Markov Models are implemented to estimate condi...

Feature selection is essential for effective and accurate text classification systems. This paper investigates the effectiveness of six commonly used feature selection methods, Evaluation used an in-house collected Arabic text classification corpus, and classification is based on Support Vector Machine Classifier. The experimental results are presented in terms of precision, recall and Macroave...

In this paper we present CroNER, a named entity recognition and classification system for Croatian language based on supervised sequence labeling with conditional random fields (CRF). We use a rich set of lexical and gazetteer-based features and different methods for enforcing document-level label consistency. Extensive evaluation shows that our method achieves state-of-the-art results (MUC F1 ...

The NAK team participated in the NTCIR-11 RITE-VAL task. This paper describes our textual entailment system and discusses the official results. Our system adopts statistical method: classification of the support vector machine (SVM). For Japanese SV subtask, our best result was 63.19 for macro-F1 score and 74.55 for accuracy. For Japanese FV subtask, our best result was 53.07 for macro-F1 score...