TeMA 2013 - Text Mining and Applications

Human languages are complex by nature and efforts in pure symbolic approaches alone have been unable to provide fully satisfying results. Text Mining and Machine Learning techniques applied to texts, raw or annotated, brought up new insights and completely shifted the approaches to Human Language Technologies. Both approaches, symbolic and statistically based, when duly integrated, have shown capabilities to bridge the gap between language theories and effective use of languages, and can enable important applications in real-world heterogeneous environment such as the Web.
The 4th Track of Text Mining and Applications (TeMA’13) is a forum for researchers working in Human Language Technologies i.e. Natural Language Processing (NLP), Computational Linguistics (CL), Natural Language Engineering (NLE), Text Mining (TM) and related areas.
Topics of Interest
General topics of interest include but are not limited to:
Text Mining:
- Multi-word Units
- Lexical Knowledge Acquisition
- Word and Multi-word Sense Disambiguation.
- Sentiment Analysis
- Text Mining Models for Social Network Analysis
- Acquisition and Usage of Ontologies
- Topic Segmentation
- Extraction of Translation Equivalents
- Word and Multi-word Translation Extraction
- Text Entailment
- Document Clustering and Classification
- Algorithms and Data Structures for Text Mining
- Information Extraction
Applications:
- Natural Language Processing
- Machine Translation
- Automatic Summarization
- Intelligent Information Retrieval
- Cross-language access to multilingual information
- E-training, E-learning and Question-Answering Systems
- Web Mining