Text graph: Difference between revisions
added Category:Natural language processing; removed {{uncategorized}} using HotCat |
m →External links: Typos and general fixes using AWB |
||
| Line 34: | Line 34: | ||
==External links== |
==External links== |
||
* [http://www.gabormelli.com/RKB/Text_Graph Gabor Melli's page on text graphs] Description of text graphs from a semantic processing |
* [http://www.gabormelli.com/RKB/Text_Graph Gabor Melli's page on text graphs] Description of text graphs from a semantic processing perspective. |
||
[[Category:Natural language processing]] |
[[Category:Natural language processing]] |
||
Revision as of 18:37, 11 January 2016
In natural language processing (NLP), a text graph is a graph representation of a text item (document, passage or sentence). It is typically created as a preprocessing step to support NLP tasks such as text condensation[1] term disambiguation[2] (topic-based) text summarization,[3] relation extraction [4] and textual entailment[5]
Representation
The semantics of what a text graph's nodes and edges represent can vary widely. Nodes for example can simply connect to tokenized words, or to domain-specific terms, or to entities mentioned in the text. The edges, on the other hand, can be between these text-based tokens or they can also link to a knowledge base.
TextGraphs Workshop Series
The TextGraphs Workshop series[6] is an series of regular academic workshops intended to encouraged the synergy between the fields of Natural Language Processing (NLP) and Graph Theory. The mix between the two started small, with graph theoretical framework providing efficient and elegant solutions for NLP applications that focused on single documents for part-of-speech tagging, word sense disambiguation and semantic role labelling, got progressively larger with ontology learning and information extraction from large text collections.
The 9th workshop [1] will be collocated with the Empirical Methods in Natural Language Processing conference (EMNLP-2014) in Qatar.
Applications
Text Summarization
In automated text summarization, text graphs can help to ensure that highly-topical information is included and that there is little redundancy in the covered relationships.[3]
Relationship Extraction
In information extraction tasks, text graphs can help to identify complex relationships expressed in the text.[4]
See also
References
- ^ Reimer, Ulrich; Hahn, Udo (1988). "Text condensation as knowledge base abstraction." (PDF). Fourth Conference on Artificial Intelligence Applications.
{{cite conference}}: Unknown parameter|booktitle=ignored (|book-title=suggested) (help) - ^ Massé, A. Blondin; Chicoisne, Guillaume; Gargouri, Yassine; Harnad, Stevan; Picard, Olivier; Marcotte, Odile (2008). "How Is Meaning Grounded in Dictionary Definitions?" (PDF). Proceedings of TextGraphs-3 Workshop.
{{cite conference}}: Unknown parameter|booktitle=ignored (|book-title=suggested) (help) - ^ a b Melli, Gabor; Shi,, Zhongmin; Wang, Yang; Liu, Yudong; Sarkar, Anoop; Popowich, Fred (2006). "Description of SQUASH, the SFU Question Answering Summary Handler for the DUC-2006 Summarization Task" (PDF). Proceeding of Document Understanding Conference (DUC 2006).
{{cite conference}}: Unknown parameter|booktitle=ignored (|book-title=suggested) (help)CS1 maint: extra punctuation (link) - ^ a b Melli, Gabor (2010). Supervised Ontology to Document Interlinking (PDF) (Ph.D.). Simon Fraser University.
- ^ MacCartney, Bill; renager, Trond G; de Marneffe, Marie-Catherine; Cer, Daniel; D. Manning, Christopher (2006). "Learning to recognize features of valid textual entailments" (PDF). Conference on Human Language Technology & Conference of the North American Chapter of the Association of Computational Linguistics.
{{cite conference}}: Unknown parameter|booktitle=ignored (|book-title=suggested) (help) - ^ http://www.textgraphs.org/
External links
- Gabor Melli's page on text graphs Description of text graphs from a semantic processing perspective.