PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
Graph and matrix metrics to analyze ergodic literature for children
Proceedings of the 23rd ACM conference on Hypertext and social media
Fixed versus dynamic co-occurrence windows in TextRank term weights for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Towards query model integration: topology-aware, IR-inspired metrics for declarative graph querying
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Onomatology and content analysis of ergodic literature
Proceedings of the 3rd Narrative and Hypertext Workshop
A phrase mining framework for recursive construction of a topical hierarchy
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Topic segmentation and labeling in asynchronous conversations
Journal of Artificial Intelligence Research
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Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.