Computational Linguistics and Intelligent Text Processing - Info and Reading Options

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The two volumes LNCS 9041 and 9042 constitute the proceedings of the 16th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2015, held in Cairo, Egypt, in April 2015. The total of 95 full papers presented was carefully reviewed and selected from 329 submissions. They were organized in topical sections on grammar formalisms and lexical resources; morphology and chunking; syntax and parsing; anaphora resolution and word sense disambiguation; semantics and dialogue; machine translation and multilingualism; sentiment analysis and emotion detection; opinion mining and social network analysis; natural language generation and text summarization; information retrieval, question answering, and information extraction; text classification; speech processing; and applications.

Open Data:

Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Sentiment Analysisand Emotion Detection -- The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis -- 1 Introduction -- 2 Model Overview -- 3 Microtext Analysis -- 4 Semantic Parsing -- 5 Subjectivity Detection -- 6 Anaphora Resolution -- 7 Sarcasm Detection -- 8 Topic Spotting -- 9 Aspect Extraction -- 10 Polarity Detection -- 11 Conclusion -- References -- Building Large Arabic Multi-domain Resources for Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Building the Datasets -- 3.1 Dataset Generation -- 3.2 Datasets Statistics -- 4 Building Lexicons -- 5 Experiments -- 5.1 Dataset Setups -- 5.2 Training Features -- 5.3 Classifiers -- 6 Results and Discussion -- 6.1 Best Performing Classifiers and Features -- 6.2 Accuracy of Lexicon Based Features Solely and Combined with Other Features -- 6.3 Effect of Document Length and Richness with Subjective Terms on Sentiment Classification -- 7 Conclusion and Future Work -- References -- Learning Ranked Sentiment Lexicons -- 1 Introduction -- 2 Learning Ranked Sentiment Lexicons -- 2.1 Latent Dirichlet Allocation -- 2.2 Rank-LDA -- 2.3 Sentiment Word Distributions -- 2.4 Opinion Ranking -- 2.5 Opinion Classifier -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Experiments: Opinion Ranking -- 3.3 Experiments: Opinion Classification -- 3.4 Qualitative Evaluation -- 4 Conclusions -- References -- Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning -- 1 Introduction -- 2 Motivation for Our Method -- 3 Related Work -- 3.1 Supervised Learning for Sentiment Analysis -- 3.2 Unsupervised Learning and Linguistic Rules for Sentiment Analysis -- 3.3 Concept-Level Sentiment Analysis and Sentic Computing -- 4 The Proposed Method -- 4.1 Emoticon Rules

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