@inproceedings{Schwitter:2000,
        title = {Answer Extraction -- Towards Better Evaluations of {NLP} Systems},
        author = {Rolf Schwitter and Diego Moll{\'a} and Rachel Fournier and Michael Hess},
        crossref = {ZZZ-Brill:2000},
        pages = {20-27},
        year = 2000,
        abstract = {We argue that reading comprehension tests are not particularly suited for the evaluation of NLP systems. Reading comprehension tests are specifically designed to evaluate human reading skills, and these require vast amounts of world knowledge and common-sense reasoning capabilities. Experience has shown that this kind of full-fledged question answering (QA) over texts from a wide range of domains is so difficult for machines as to be far beyond the present state of the art of NLP. To advance the field we propose a much more modest evaluation set-up, viz. Answer Extraction (AE) over texts from highly restricted domains. AE aims at retrieving those sentences from documents that contain the explicit answer to a user query. AE is less ambitious than full-fledged QA but has a number of important advantages over QA. It relies mainly on linguistic knowledge and needs only a very limited amount of world knowledge and few inference rules. However, it requires the solution of a number of key linguistic problems. This makes AE a suitable task to advance NLP techniques in a measurable way. Finally, there is a real demand for working AE systems in technical domains. We outline how evaluation procedures for AE systems over real world domains might look like and discuss their feasibility.},
	biburl = {http://www.bibsonomy.org/bibtex/2449b4548c23384e9a02234898bdc9715/diego_ma},
	keywords = {answer_extraction evaluation molla_publication},
    }

@inproceedings{Schneider:1999,
        title = {Inkrementelle Minimale Logische {F}ormen f{\"u}r die {A}ntwortextraktion},
        address = {Germersheim, Germany},
        author = {Gerold Schneider and Diego Moll{\'a} and Michael Hess},
        booktitle = {Proc. 34. Linguistisches {K}olloquium},
        year = 1999,
        biburl = {http://www.bibsonomy.org/bibtex/297e9a688c93398aaedca1cc8430e5604/diego_ma},
	keywords = {answer_extraction semantics ExtrAns molla_publication},
    }

@article{Molla:TAL2,
        title = {Extrans, an Answer Extraction System},
        author = {Diego Moll{\'a} and Rolf Schwitter and Michael Hess and Rachel Fournier},
        journal = {Traitement Automatique des Langues},
        number = 2,
        pages = {495-522},
        volume = 41,
        year = 2000,
        biburl = {http://www.bibsonomy.org/bibtex/28c68dc2b9b93d0fb3dc28858c35a0a42/diego_ma},
	keywords = {ExtrAns answer_extraction molla_publication},
    }

@proceedings{ZZZ-Brill:2000,
        title = {Proc. {ANLP/NAACL} 2000 Workshop on Reading Comprehension Tests as Evaluation for Computer-Based Language Understanding Systems},
        address = {Seattle, WA},
        booktitle = {Proc. {ANLP/NAACL} 2000 Workshop on Reading Comprehension Tests as Evaluation for Computer-Based Language Understanding Systems},
        editor = {Eric Brill and Eugene Charniak and Mary Harper and Marc Light and Ellen Riloff and Ellen Voorhees},
        organization = {ACL},
        year = 2000,
        biburl = {http://www.bibsonomy.org/bibtex/24fb0f326a00848adbad66d4c583c203b/diego_ma},
	keywords = {evaluation answer_extraction question_answering},
    }

@inproceedings{Zajac:2001,
        title = {Towards Ontological Question Answering},
        address = {Toulouse},
        author = {R{\'e}mi Zajac},
        booktitle = {Proc. ACL2001, Workshop on Open Domanin QA},
        year = 2001,
        abstract = {This paper presents and ontology-based semantic framework to question answering. Both questions and source text are parsed into underspecified semantic expressions where names of semantic atoms and predicates are defined in an interlingual ontology. Answer retrieval is done using subsumption and unification, and queries are expanded incrementally using ontological rules. Ranking of answers is achieved by using graded unification.},
	biburl = {http://www.bibsonomy.org/bibtex/2a4b43bb88a05f61472a4ea0a66977d77/diego_ma},
	keywords = {answer_extraction ontology},
    }

@article{Voorhees:2001,
        title = {The {TREC} Question Answering Track},
        author = {Ellen M. Voorhees},
        journal = {Natural Language Engineering},
        number = 4,
        pages = {361-378},
        volume = 7,
        year = 2001,
        url = {http://citeseer.ist.psu.edu/600390.html},
        biburl = {http://www.bibsonomy.org/bibtex/2e0755d8af4024080bbe89f346c997b42/diego_ma},
	keywords = {inf_retrieval answer_extraction},
    }

@unpublished{Voorhees:draft,
        title = {Overview of the {TREC} 2001 Question Answering Track},
        author = {Ellen M. Voorhees},
        note = {Draft},
        year = 2002,
        url = {http://trec.nist.gov/trec10/t10_notebook.html},
        biburl = {http://www.bibsonomy.org/bibtex/26fe5b94948819e09070e3577366f3095/diego_ma},
	keywords = {inf_retrieval answer_extraction},
    }

@mastersthesis{Purver:2000,
        title = {Simplistic Question Answering},
        author = {Matthew Purver},
        school = {University of Cambridge},
        year = 2000,
        url = {http://www.dcs.kcl.ac.uk/pg/purver/},
        
      month = {October},
    abstract = {The aim of this project was to develop a system that could identify text passages which answer a question. The approach taken used ideas from various participants in the recent TREC-8 conference, and added the use of notions on sentence structure --- particularly structural information and matching. The system was successfully tested on training data incorporating a wide range of sentence structure phenomena. Performance was evaluated in blind tests on two sets of data and results were encouraging, with good levels of both recall and precision being achieved.},
	biburl = {http://www.bibsonomy.org/bibtex/218533b8cc3f2a7c501a3c2462939e216/diego_ma},
	keywords = {answer_extraction},
    }

@misc{Pratt:1999,
        title = {Inference Problems in {ExtrAns}: Quick Summary},
        author = {Ian Pratt-Hartmann},
        note = {Draft for internal use only},
        year = 1999,
        
      month = {August},
    biburl = {http://www.bibsonomy.org/bibtex/24a3ed337ae5b8a373287532a4a5b1182/diego_ma},
	keywords = {answer_extraction ambiguity logic},
    }

@article{OConnor:1975,
        title = {Retrieval of Answer Sentences and Answer-Figures from Papers by Text Searching},
        author = {John O'Connor},
        journal = {Information Processing \& Management},
        number = {5/7},
        pages = {155-164},
        volume = 11,
        year = 1975,
        biburl = {http://www.bibsonomy.org/bibtex/288be4568d6b0b016f2cdcfe8e4a4e4db/diego_ma},
	keywords = {answer_extraction},
    }

@inproceedings{Mann:2001,
        title = {A Statistical Method for Short Answer Extraction},
        address = {Toulouse, France},
        author = {Gideon S. Mann},
        booktitle = {Proc. Workshop on Open-Domain Question Answering at ACL 2001},
        year = 2001,
        url = {http://www.cs.jhu.edu/\~{}gsm/publications/home.html},
        abstract = {This paper presents a simple, general method for using the Mutual Information (MI) statistic trained on unannotated trivia questions to estimate question class/ semantic tag correlation...},
	biburl = {http://www.bibsonomy.org/bibtex/2100911b3cfa200051c9b6bed128b5672/diego_ma},
	keywords = {answer_extraction},
    }

@inproceedings{Hirschman:1999,
        title = {Deep {R}ead: A Reading Comprehension System},
        author = {Lynette Hirschman and Marc Light and Eric Breck and John D. Burger},
        booktitle = {Proc. ACL'99},
        organization = {University of Maryland},
        year = 1999,
        abstract = {This paper describes initial work on {\bf Deep Read}, and automated reading comprehension system that accepts arbitrary text input (a story) and answers questions about it. We have adquired a corpus of 60 development and 60 test stories of $3^{rd}$ to $6^{th}$ grade material; each story is followed by short-answer questions (an answer key was also provided). We used these to construct and evaluate a baseline system that uses pattern matching (bag-of-words) techniques augmented with additional automated linguistic processing (stemming, name identification, semantic class identification, and pronoun resolution). This simple system retrieves the sentence containing the answer 30-40\% of the time.},
	biburl = {http://www.bibsonomy.org/bibtex/2c0fe448171f04d2db2f304bd8146ffab/diego_ma},
	keywords = {answer_extraction},
    }

@inproceedings{Harabagiu:2000b,
        title = {Experiments with Open-Domain Textual Question Answering},
        author = {Sanda M. Harabagiu and Marius A. Pa{\c{s}}ca and Steven I. Maiorano},
        booktitle = {Proc. COLING-2000},
        year = 2000,
        url = {http://www.seas.smu.edu/\~{}sanda/papers.html},
        abstract = {This paper describes the integration of several knowledge-based natural language processing techniques into a Question Answering system, capable of mining textual answers from large collections of texts. Surprizing quality is achieved when several lightweight knowledge-based NLP techniques complement mostly shallow, surface-based approaches.},
	biburl = {http://www.bibsonomy.org/bibtex/2744c289f1b9739185b7425f969fd6a56/diego_ma},
	keywords = {answer_extraction},
    }

@incollection{Echihabi:2004,
        title = {How to Select an Answer String?},
        author = {Abdessamad Echihabi and Ulf Hermjakob and Eduard Hovy and Daniel Marcu and Eric Melz and Deepak Ravichandran},
        booktitle = {Advances in Textual Question Answering},
        editor = {Tomek Strzalkowski and Sanda Harabagiu},
        publisher = {Kluwer},
        year = 2004,
        url = {http://www.isi.edu/~marcu/papers.html},
        abstract = {Given a question Q and a sentence/paragraph SP that is likely to contain the answer to Q, an answer selection module is supposed to select the ``exact'' answer sub-string A $\subset$ SP. We study three distinct approaches to solving this problem: one approach uses algorithms that rely on rich knowledge bases and sophisticated syntactic/semantic processing; one approach uses patterns that are learned in an unsupervised manner from the web, using computational biology-inspired alignment algorithms; and one approach uses statistical noisy-channel algorithms similar to those used in machine translation. We assess the strengths and weaknesses of these three approaches and show how they can be combined using a maximum entropy-based framework.},
	biburl = {http://www.bibsonomy.org/bibtex/2380b4b132d8f3219a5a9c6a471dadc14/diego_ma},
	keywords = {answer_extraction},
    }

@inproceedings{Breck:1999,
        title = {Question Answering from Large Document Collections},
        author = {Eric Breck and John Burger and David House and Marc Light and Inderjeet Mani},
        booktitle = {Proc. 1999 {AAAI} Fall Symposium on Question Answering Systems},
        note = {Forthcoming},
        year = 1999,
        biburl = {http://www.bibsonomy.org/bibtex/27df818c34f3b722faade6124807a547f/diego_ma},
	keywords = {question_answering answer_extraction},
    }

@misc{Breck:1999:2,
        title = {A Sys Called {Qanda}},
        author = {Eric Breck and John Burger and Lisa Ferro and David House and Marc Light and Inderjeet Mani},
        year = 1999,
        biburl = {http://www.bibsonomy.org/bibtex/25536727cce39c9bf5a8cc219f47d5f5d/diego_ma},
	keywords = {answer_extraction question_answering},
    }