TUBE (Text-cUBE) for discovering documentary evidence of associations among entities Export

@inproceedings{citeulike:1705192, abstract = {User-driven discovery of associations among entities, and documents that provide evidence for these associations, is an important search task conducted by researchers and do-main information specialists. Entities here refer to real or abstract objects such as people, organizations, ideologies, etc. Associations are the inter-relationships among entities. Most current works in query-driven document retrieval and finding representative subgraphs are ill-suited for the task as they lack an awareness of entity types as well as an intuitive representation of associations. We propose the TUBE model, a text cube approach for discovering associations and documentary evidence of these associations. The model consists of a multi-dimensional view of document data, a flexible representation of multi-document summaries, and a set of operations for data manipulation. We conduct a case study on real-life data to illustrate its applicability to the above task and compare it with the non-TUBE approach.}, address = {New York, NY, USA}, author = {Lauw, Hady W. and Lim, Ee-Peng and Pang, Hweehwa}, booktitle = {SAC '07: Proceedings of the 2007 ACM symposium on Applied computing}, citeulike-article-id = {1705192}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1244002.1244185}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1244002.1244185}, doi = {10.1145/1244002.1244185}, isbn = {1595934804}, keywords = {active-annotation, named-entity-tagging, user-feedback}, pages = {824--828}, posted-at = {2007-09-28 15:50:23}, priority = {0}, publisher = {ACM Press}, title = {TUBE (Text-cUBE) for discovering documentary evidence of associations among entities}, url = {http://dx.doi.org/10.1145/1244002.1244185}, year = {2007} }

TY - CONF ID - citeulike:1705192 L3 - citeulike-article-id:1705192 N2 - User-driven discovery of associations among entities, and documents that provide evidence for these associations, is an important search task conducted by researchers and do-main information specialists. Entities here refer to real or abstract objects such as people, organizations, ideologies, etc. Associations are the inter-relationships among entities. Most current works in query-driven document retrieval and finding representative subgraphs are ill-suited for the task as they lack an awareness of entity types as well as an intuitive representation of associations. We propose the TUBE model, a text cube approach for discovering associations and documentary evidence of these associations. The model consists of a multi-dimensional view of document data, a flexible representation of multi-document summaries, and a set of operations for data manipulation. We conduct a case study on real-life data to illustrate its applicability to the above task and compare it with the non-TUBE approach. SN - 1595934804 AD - New York, NY, USA EP - 828 TI - TUBE (Text-cUBE) for discovering documentary evidence of associations among entities PB - ACM Press T2 - SAC '07: Proceedings of the 2007 ACM symposium on Applied computing SP - 824 KW - active-annotation KW - named-entity-tagging KW - user-feedback AU - Lauw, Hady AU - Lim, Ee-Peng AU - Pang, Hweehwa PY - 2007/// UR - http://dx.doi.org/10.1145/1244002.1244185 ER -