Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Infoxtract: A customizable intermediate level information extraction engine
Natural Language Engineering
Tree kernels for semantic role labeling
Computational Linguistics
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Convolution kernels for opinion holder extraction
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A vector space model for subjectivity classification in Urdu aided by co-training
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Analyzing Urdu social media for sentiments using transfer learning with controlled translations
LSM '12 Proceedings of the Second Workshop on Language in Social Media
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Automatic extraction of opinion holders and targets (together referred to as opinion entities) is an important subtask of sentiment analysis. In this work, we attempt to accurately extract opinion entities from Urdu newswire. Due to the lack of resources required for training role labelers and dependency parsers (as in English) for Urdu, a more robust approach based on (i) generating candidate word sequences corresponding to opinion entities, and (ii) subsequently disambiguating these sequences as opinion holders or targets is presented. Detecting the boundaries of such candidate sequences in Urdu is very different than in English since in Urdu, grammatical categories such as tense, gender and case are captured in word inflections. In this work, we exploit the morphological inflections associated with nouns and verbs to correctly identify sequence boundaries. Different levels of information that capture context are encoded to train standard linear and sequence kernels. To this end the best performance obtained for opinion entity detection for Urdu sentiment analysis is 58.06% F-Score using sequence kernels and 61.55% F-Score using a combination of sequence and linear kernels.