Agichtein, E., Lawrence, S., and Gravano, L. "Learning Search Engine Specific Query Transformations for Question Answering". In Proc. of the 10th International World-Wide Web Conference (WWW10), 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....query to bridge the gap between the query space and document space. We use general open resources to overcome this problem. The external general resources that can be readily used include the Web, WordNet, Knowledge bases, and Query Logs. Many groups working on QA have recently used the Web [3][4] 5] 6] 8] 9] 12] 18] and WordNet [7] 10] 11] 16] 17] as resources for question answering. In our system, we integrate the external resources to expand the query. The new query is then used to look for the relevant documents and sentences in the QA Text Collection. 3.1. Using Web as the ....

E. Agichtein, S. Lawrence and L. Gravano (2001)." Learning search engine specific query transformations for question answering". In Proceedings of the 10th World Wide Web Conference (WWW10), 169-178.

....query to bridge the gap between the query space and document space. We use general open resources to overcome this problem. The external general resources that can be readily used include the Web, WordNet, Knowledge bases, and Query Logs. Many groups working on QA have recently used the Web [3][4] 5] 6] 8] 9] 12] 18] and WordNet [7] 10] 11] 16] 17] as resources for question answering. In our system, we integrate the external resources to expand the query. The new query is then used to look for the relevant documents and sentences in the QA Text Collection. 3.1. Using Web as the ....

E. Agichtein, S. Lawrence and L. Gravano (2001). "Learning search engine specific query transformations for question answering". In Proceedings of the 10th World Wide Web Conference (WWW10), 169-178.

....contains words not included in the indexed list, AnswerBus uses the search engines average returns for all the indexed words to determine which search engines are most appropriate. 2.2 Search engine specific query formation Most search engines are not designed for natural language questions. [2] shows that QA systems using good queries will significantly outperform the underlying web search engines and a commercial search engine specializing in question answering. 6] argues that using extra information to direct the search process provides more valuable results than by considering only ....

Eugene Agichtei, Steve Lawrence, Luis Gravano. Learning Search Engine Specific Query Transformations for Question Answering. Tenth World Wide Web Conference. May 1-5, 2001, Hong Kong, China.

....in order to determine the possible parts of speech of a word as well as its morphological variants. We created the rewrite rules and associated weights manually for the current system, although it may be possible to learn query to answer reformulations and weights (e.g. see Agichtein et al. [4]; Radev et al. 17] The rewrites generated by our system are simple string based manipulations. For instance, some question types involve query rewrites with possible verb movement; the verb is in the question Where is the Louvre Museum located should be moved in formulating the desired ....

E. Agichtein, S. Lawrence and L. Gravano (2001). Learning search engine specific query transformations for question answering. In Proceedings of the lO th World Wide Web Conference (WWWlO), 169-178.

....under way and try to port existing Q A techniques to a much larger context the World Wide Web. Our earlier study [11] already shows that the current WWW search engines, especially those with very large index like Google, o#ers a very promising source for question answering. Agichtein et al. [12] presented a technique on how to learn search engine specific query transformations for question answering. A similar transformation technique also appeared in [13] The idea is that the current query interfaces of most generic search engines, such as Google, etc. does not provide enough ....

....context. For example, the user question is processed by a parser to learn its syntactic structure. In order to help extract the answers and make the classification task easier, the questions are classified into three categories: nominal, numerical and temporal. Similar to the techniques used in [12, 7], various query modulation techniques, such as query expansion, noun phrase formation, transformation are applied to the initial questions to get high quality results for later answer extraction. Their answer extraction module utilize both IDF (inverse document frequency, a measure of the spread ....

[Article contains additional citation context not shown here]

Eugene Agichtein, Steve Lawrence, and Luis Gravano. Learning search engine specific query transformations for question answering. In the Proceedings of the 10th World Wide Web Conference (WWW

....an index via crawling. Generic index Index building: Flipdog, CiteSeer Meta searching: Mysimon Query time crawling: AHOY Generic major search engines: Google, Alta Vista No specialized query modification No current systems Specialized query modification Spedcialized index [1, 5, 10] Figure 2: Specialized search engine example systems. Examples of specialized search engines constructed in this way are CiteSeer [12] for the searching of Computer Science papers, Deadliner [8] for finding conference deadlines, HPsearch (http: hpsearch.unitrier. de hp) for finding personal ....

....types of natural language queries can be developed. This once again uses the learning of query modifications. At query time the user s query is extended using the learnt query modifications and the modified queries are sent to various major search engines and the results merged. Agichtein et al. [1] investigate the automatic learning of query modifications for common questions such as who was. where is. etc. The general idea is to transform questions into specific phrases that may be found on Web pages containing the answer. These specialized search engines can easily be ....

E. Agichtein, S. Lawrence, L. Gravano. Learning Search Engine Specific Query Transformations for Question Answering. To appear in Proceedings of WWW10, Hong Kong, 2001.

.... tuples in the relation) The extracted relation can be viewed as a set of prepared answers for a particular class of questions (e.g. What is the location of the headquarters of X ) While question answering techniques may be useful for retrieving specific tuples in the target relation (e.g. [4, 20]) the problem of retrieving documents that collectively contain the complete relation has not been addressed in the question answering literature, to the best of our knowledge. Our work is also related to recent research on focused web crawling (e.g. 6] which addresses the problem of ....

E. Agichtein, S. Lawrence, and L. Gravano. Learning search engine specific query transformations for question answering. In Proceedings of the 10th World Wide Web Conference (WWW-10), 2001.

No context found.

Agichtein, E., Lawrence, S., and Gravano, L. "Learning Search Engine Specific Query Transformations for Question Answering". In Proc. of the 10th International World-Wide Web Conference (WWW10), 2001.

No context found.

Agichtein, E., Lawrence, S. & Gravano, L. (2001), Learning search engine specific query transformations for question answering, in `World Wide Web', pp. 169--178.

No context found.

Agichtein, E., Lawrence, S., and Gravano, L. "Learning Search Engine Specific Query Transformations for Question Answering". In Proc. of the 10th International World-Wide Web Conference (WWW10), 2001.

No context found.

E. Agichtein, S. Lawrence, and L. Gravano. Learning search engine specific query transformations for question answering. In Proceedings of the 10th World Wide Web Conference (WWW10), pages 169--178, 2001.

No context found.

Agichtein, E., Lawrence, S. , Gravano, L. Learning Search Engine Specific Query Transformations for Question Answering. 10 WWW Conference, 2001.

No context found.

E. Agichtein, S. Lawrence, and L. Gravano. Learning search engine specific query transformations for question answering. In Proceedings of the 10th World Wide Web Conference (WWW10), pages 169--178, 2001.

No context found.

Agichtein, E., Lawrence S., & Gravano, L. (2001) Learning search engine specific query transformations for question answering. In Proceedings of the tenth international conference on World Wide Web, NY: ACM Press, 169-178.

No context found.

E. Agichtein, S. Lawrence, and L. Gravano. Learning search engine specific query transformations for question answering. In Proceedings of the 10th International World-Wide Web Conference (WWW10), 2001.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC