The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Scaling question answering to the Web
Abstract
The wealth of information on the web makes it an attractive resource for seeking quick answers to simple, factual questions such as "who was the first American in space?" or "what is the second tallest mountain in the world?" Yet today's most advanced web search services (e.g., Google and AskJeeves) make it surprisingly tedious to locate answers to such questions. In this paper, we extend question-answering techniques, first studied in the information retrieval literature, to the web and experimentally evaluate their performance. First we introduce MULDER, which we believe to be the first general-purpose, fully-automated question-answering system available on the web. Second, we describe MULDER's architecture, which relies on multiple search-engine queries, natural-language parsing, and a novel voting procedure to yield reliable answers coupled with high recall. Finally, we compare MULDER's performance to that of Google and AskJeeves on questions drawn from the TREC-8 question track. We find that MULDER's recall is more than a factor of three higher than that of AskJeeves. In addition, we find that Google requires 6.6 times as much user effort to achieve the same level of recall as MULDER. 1.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Introduction to WordNet: An On-line Lexical Database*
Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990) · International Journal of Lexicography · doi:10.1093/ijl/3.4.235 · Cited by 4,197
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1998) · Symposium on Discrete Algorithms · doi:10.5555/314613.315045 · Cited by 1,814
Focused crawling: a new approach to topic-specific Web resource discovery
Chakrabarti, S., van den Berg, M., & Dom, B. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00052-3 · Cited by 1,486
Query Expansion using Lexical-Semantic Relations
Voorhees, E. M. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_7 · Cited by 947
Nymble: a high-performance learning name-finder
Bikel, D. M., Miller, S., Schwartz, R., & Weischedel, R. (1997) · Proceedings of the fifth conference on Applied natural language processing - · doi:10.3115/974557.974586 · Cited by 726
Grouper: a dynamic clustering interface to Web search results
Zamir, O., & Etzioni, O. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00054-7 · Cited by 720
Self-citationA new statistical parser based on bigram lexical dependencies
Collins, M. J. (1996) · Proceedings of the 34th annual meeting on Association for Computational Linguistics - · doi:10.3115/981863.981888 · Cited by 614
Building a Large Annotated Corpus of English: The
MarcusMitchell, P., Ann, M., & SantoriniBeatrice (1993) · Computational Linguistics · doi:10.5555/972470.972475 · Cited by 489
Cited by
Sparse, Dense, and Attentional Representations for Text Retrieval
Luan, Y., Eisenstein, J., Toutanova, K., & Collins, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00369 · Cited by 363
(2003) · Cited by 62
Yamamoto, Y., Tezuka, T., Jatowt, A., & Tanaka, K. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72524-4_28 · Cited by 14
Metadata
- Authors
- Kwok, C. C. T., Etzioni, O., & Weld, D. S.
- Venue
- Proceedings of the 10th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2001-04
- Pages
- 150-161
- OpenAlex
- W2055354597
- Cited by
- 326
- References
- 18
- FWCI
- 27.63
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Scaling question answering to the Web. https://app.cochat.ai/papers/10.1145/371920.371973
- The paper
- Kwok, C. C. T., Etzioni, O., & Weld, D. S. (2001). Scaling question answering to the Web. Proceedings of the 10th international conference on World Wide Web. https://doi.org/10.1145/371920.371973
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
