@article{girju2006automatic,
  abstract = {An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part–whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part–whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part–whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part–whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.},
  author = {Girju, Roxana and Badulescu, Adriana and Moldovan, Dan I.},
  ee = {http://dx.doi.org/10.1162/coli.2006.32.1.83},
  file = {girju2006automatic.pdf:girju2006automatic.pdf:PDF},
  groups = {public},
  interhash = {e3b517e5895171e35375ce08d632d738},
  intrahash = {ce346613f91431251a6fe867f4360378},
  journal = {Computational Linguistics},
  journalpub = {1},
  number = 1,
  pages = {83-135},
  timestamp = {2010-10-25 15:08:53},
  title = {Automatic Discovery of Part-Whole Relations.},
  url = {http://dblp.uni-trier.de/db/journals/coling/coling32.html#GirjuBM06},
  username = {dbenz},
  volume = 32,
  year = 2006
}

@inproceedings{snow2006semantic,
  abstract = {We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word’s coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10; 000 novel synsets to WordNet 2.1 at 84% precision, a relative error reduction of 70% over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23% relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs.},
  author = {Snow, Rion and Jurafsky, Daniel and Ng, Andrew Y.},
  booktitle = {ACL},
  crossref = {conf/acl/2006},
  ee = {http://acl.ldc.upenn.edu/P/P06/P06-1101.pdf},
  file = {snow2006semantic.pdf:snow2006semantic.pdf:PDF},
  groups = {public},
  interhash = {c0f5a3a22faa8dc4b61c9a717a6c9037},
  intrahash = {8f39e7ac43a97719c5a746da02dbd964},
  publisher = {The Association for Computer Linguistics},
  timestamp = {2010-10-25 15:06:10},
  title = {Semantic Taxonomy Induction from Heterogenous Evidence.},
  url = {http://dblp.uni-trier.de/db/conf/acl/acl2006.html#SnowJN06},
  username = {dbenz},
  year = 2006
}