@inproceedings{Shen:2006,
  abstract = {In this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training. Experimental results show that our method significantly outperforms state-ofthe-art syntactic relation-based methods by up to 20% in MRR.},
  added-at = {2009-11-11T22:33:09.000+0100},
  address = {Sydney},
  author = {Shen, Dan and Klakow, Dietrich},
  biburl = {http://www.bibsonomy.org/bibtex/2b385d2d62a1cec0bcaa6f01019112f65/diego_ma},
  booktitle = {Proceedings COLING/ACL 2006},
  interhash = {6966be5911653238e919c7e841c639d8},
  intrahash = {b385d2d62a1cec0bcaa6f01019112f65},
  keywords = {question_answering machine_learning dependencies DG},
  pages = {889-896},
  timestamp = {2009-11-11T22:33:09.000+0100},
  title = {Exploring Correlation of Dependency Relation Paths for Answer Extraction},
  url = {http://acl.ldc.upenn.edu/P/P06/P06-1112.pdf},
  year = 2006
}

@phdthesis{Clegg:2008,
  abstract = {As the body of published literature grows at an accelerating rate, increasingly sophisticated computational methods for natural language processing are required to manage and mine the written knowledge available to life sciences researchers. One important topic within this field is the problem of relationship extraction. Given a text about molecular biology, the challenge is to automatically retrieve the biophysical, biochemical or genetic interactions described therein. Much progress has been made on this problem and others like it by using statistical Information retrieval techniques, regular expressions, finite state automata, sequence alignment and other relatively superficial approaches. However, there are a variety of more linguistically-informed methods available which treat each sentence as a tree or graph rather than simply a collection or sequence of words. Various natural-language parsers are available which facilitate this kind of solution, and the experimental work in this thesis begins with a comparison of several of these on a standard molecular biology corpus using established benchmarking techniques. This is followed by some experiments using evaluation measures tailored to specific biologically-important tasks. A processing pipeline is then described which uses the best of these parsers, along with several other open-source tools, to produce highquality dependency graph representations of input sentences. Finally, three novel deterministic algorithms for relationship extraction are presented. Two of these take dependency graphs as input and return interactions between pre-tagged gene and protein entities, outperforming most existing methods on a standard publically-available test corpus; the other is a strong baseline method using no linguistic information. An appendix discusses the related problems of entity recognition and identification, which --- while outside the main scope of this thesis ---are prerequisites for the development of relationship extraction applications.},
  added-at = {2009-07-22T09:57:26.000+0200},
  author = {Clegg, Andrew B.},
  biburl = {http://www.bibsonomy.org/bibtex/2d60cf1178f7d5c1d174fece60f574382/diego_ma},
  interhash = {1cb0d306a6012be0407119d8c663fade},
  intrahash = {d60cf1178f7d5c1d174fece60f574382},
  keywords = {dependencies inf-extraction parsing biomedical},
  library = {Bibsonomy (May 2009)},
  school = {University of London},
  timestamp = {2009-07-22T09:57:26.000+0200},
  title = {Computational-Linguistic Approaches to Biological Text Mining},
  year = 2008
}

@phdthesis{Clegg:2008,
  abstract = {As the body of published literature grows at an accelerating rate, increasingly sophisticated computational methods for natural language processing are required to manage and mine the written knowledge available to life sciences researchers. One important topic within this field is the problem of relationship extraction. Given a text about molecular biology, the challenge is to automatically retrieve the biophysical, biochemical or genetic interactions described therein.  Much progress has been made on this problem and others like it by using statistical Information retrieval techniques, regular expressions, finite state automata, sequence alignment and other relatively superficial approaches. However, there are a variety of more linguistically-informed methods available which treat each sentence as a tree or graph rather than simply a collection or sequence of words.  Various natural-language parsers are available which facilitate this kind of solution, and the experimental work in this thesis begins with a comparison of several of these on a standard molecular biology corpus using established benchmarking techniques. This is followed by some experiments using evaluation measures tailored to specific biologically-important tasks. A processing pipeline is then described which uses the best of these parsers, along with several other open-source tools, to produce highquality dependency graph representations of input sentences.  Finally, three novel deterministic algorithms for relationship extraction are presented. Two of these take dependency graphs as input and return interactions between pre-tagged gene and protein entities, outperforming most existing methods on a standard publically-available test corpus; the other is a strong baseline method using no linguistic information. An appendix discusses the related problems of entity recognition and identification, which --- while outside the main scope of this thesis ---are prerequisites for the development of relationship extraction applications.},
  added-at = {2009-05-15T07:19:30.000+0200},
  author = {Clegg, Andrew B.},
  biburl = {http://www.bibsonomy.org/bibtex/228290bb7bc4c05628972a3ffae4e521d/diego_ma},
  interhash = {ef58c1d24f87d55fe3736d1e10d8d355},
  intrahash = {28290bb7bc4c05628972a3ffae4e521d},
  keywords = {dependencies inf-extraction parsing biomedical},
  library = {Bibsonomy (May 2009)},
  school = {University of London},
  timestamp = {2009-05-15T07:19:30.000+0200},
  title = {Computational-Linguistic Approaches to Biological Text Mining},
  year = 2008
}

@inproceedings{Marneffe:2006,
  added-at = {2009-05-15T06:33:15.000+0200},
  author = {de Marneffe, Marie-Catherine and MacCartney, Bill and Manning, Christopher D.},
  biburl = {http://www.bibsonomy.org/bibtex/2eebfa8ea98b57038c7ab6b564fb1a6b2/diego_ma},
  booktitle = {LREC},
  description = {literature-log.bib 1/21/2009},
  interhash = {4bc6b36e59f42e52b235507aca13199d},
  intrahash = {eebfa8ea98b57038c7ab6b564fb1a6b2},
  keywords = {dependencies constituency parsers},
  library = {Bibsonomy (May 2009)},
  timestamp = {2009-05-15T06:33:15.000+0200},
  title = {Generating Typed Dependency Parses from Phrase Structure Trees},
  url = {http://nlp.stanford.edu/pubs/LREC06_dependencies.pdf},
  year = 2006
}

@inproceedings{Molla:ANLP02,
  added-at = {2008-01-29T08:20:55.000+0100},
  author = {Moll{\'a}, Diego and Hutchinson, Ben},
  biburl = {http://www.bibsonomy.org/bibtex/251a7573fde0a098e561c31f0d776ad5c/diego_ma},
  booktitle = {Proc. 2002 Australasian NLP Workshop},
  interhash = {bc6690c5c8367272a97adf7cbf1ca3b7},
  intrahash = {51a7573fde0a098e561c31f0d776ad5c},
  keywords = {AnswerFinder dependencies molla_publication},
  timestamp = {2008-01-29T08:20:55.000+0100},
  title = {Dependency-based Semantic Interpretation for Answer Extraction},
  year = 2002
}

@inproceedings{Strzalkowski:TREC5,
  added-at = {2007-12-14T02:47:02.000+0100},
  address = {Gaithersburg},
  author = {Strzalkowski, Tomek and Guthrie, Louise and Karlgren, Jussi and Leistensnider, Jim and Lin, Fang and Perez-Carballo, Jose and Straszheim, Troy and Wang, Jin and Wilding, Jon},
  biburl = {http://www.bibsonomy.org/bibtex/298b5838b5006c3f62f26f5071ebac707/diego_ma},
  booktitle = {Proc. {TREC-5}},
  editor = {Voorhees, Ellen M. and Harman, Donna K.},
  interhash = {52b29dfc6f1ea99305035d9815d9ebf5},
  intrahash = {98b5838b5006c3f62f26f5071ebac707},
  keywords = {inf_retrieval NLP dependencies},
  month = {November},
  number = {500-238},
  pages = {291-313},
  series = {NIST Special Publication},
  timestamp = {2007-12-14T02:47:02.000+0100},
  title = {Natural Language Information Retrieval: {TREC-5} Report},
  year = 1997
}

@inproceedings{Schneider:2003,
  abstract = {Many extensions to text-based, data-intensive knowledge management approaches, such as Information Retrieval or Data Mining, focus on integrating the impressive recent advances in language technology. For this, they need fast, robust parsers that deliver linguistic data which is meaningful for the subsequent processing stages. This paper introduces such a parsing system. Its output is a hierarchical structure of syntactic relations, functional dependency structures ...},
  added-at = {2007-12-14T02:46:16.000+0100},
  author = {Schneider, Gerold},
  biburl = {http://www.bibsonomy.org/bibtex/21c7c841480ab52567c6eba2f9a46d1ea/diego_ma},
  booktitle = {Proc. Workshop on Treebanks and Linguistic Theories (TLT 2003)},
  interhash = {77795f5e181dfcaa6a1c688c9281b98f},
  intrahash = {1c7c841480ab52567c6eba2f9a46d1ea},
  keywords = {dependencies parsers},
  timestamp = {2007-12-14T02:46:16.000+0100},
  title = {Extracting and Using Trace-Free Functional Dependencies from the Penn Treebank to Reduce Parsing Complexity},
  url = {http://w3.msi.vxu.se/\~{}rics/TLT2003/},
  year = 2003
}

@inproceedings{Lin:1995,
  added-at = {2007-12-14T02:42:30.000+0100},
  address = {Montreal, Canada},
  author = {Lin, Dekang},
  biburl = {http://www.bibsonomy.org/bibtex/20dba702695aae3ddec65247f5896a3a3/diego_ma},
  booktitle = {Proc. IJCAI-95},
  interhash = {5effeead6d23be16738e2b0e8941e3fb},
  intrahash = {0dba702695aae3ddec65247f5896a3a3},
  keywords = {dependencies},
  pages = {1420-1425},
  timestamp = {2007-12-14T02:42:30.000+0100},
  title = {A Dependency-Based Method for Evaluating Broad-Coverage Parsers},
  year = 1995
}

@inproceedings{Dras:2000,
  abstract = {This paper looks at integrating dependency and constituency into a common framework, using the TAG formalism and a different perspective on the metagrammar of (Dras, 1999) in which the meta level models dependencies and the object level model constituency...},
  added-at = {2007-12-14T02:38:22.000+0100},
  address = {Birmingham, UK},
  author = {Dras, Mark and Chiang, David and Schuler, William},
  biburl = {http://www.bibsonomy.org/bibtex/2c95fe279295b6fbf07dd00c37c3cedc8/diego_ma},
  booktitle = {Proc. Workshop on Linguistic Theory and Grammar Implementation},
  interhash = {647937708b2aa03dc66b2cf1c7a3c1c5},
  intrahash = {c95fe279295b6fbf07dd00c37c3cedc8},
  keywords = {TAG dependencies},
  pages = {33-46},
  timestamp = {2007-12-14T02:38:22.000+0100},
  title = {A Multi-Level TAG Approach to Dependency},
  url = {http://www.ics.mq.edu.au/~madras/research/bib.html},
  year = 2000
}

@article{Dras:2002,
  abstract = {This paper looks at integrating dependency and constituency into a common framework, using the TAG formalism and a different perspective on the meta-level grammar of Dras (1999a) in which the meta level models dependencies and the object level models constituency...},
  added-at = {2007-12-14T02:38:20.000+0100},
  author = {Dras, Mark and Chiang, David and Schuler, William},
  biburl = {http://www.bibsonomy.org/bibtex/2b9d27f518a4a8f188f447328a7aa8129/diego_ma},
  interhash = {6361f2226ade95bce7d1f917e9923209},
  intrahash = {b9d27f518a4a8f188f447328a7aa8129},
  journal = {Journal of Language and Computation},
  keywords = {TAG dependencies},
  timestamp = {2007-12-14T02:38:20.000+0100},
  title = {On Relations of Constituency and Dependency Grammars},
  year = 2002
}