Weakly-supervised discovery of named entities using web search queries

Paşca, Marius

doi:10.1145/1321440.1321536

Cited by 84 publications

(86 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also compare our results to Pasca [7] as shown in Table 3. There are 197 countries in his evaluation dataset, and the input to his system is a set of seed named entities.…”

Section: Resultsmentioning

confidence: 99%

“…As mentioned earlier, their system requires the name of the semantic set and an additional seed instance. Pasca [7,6] illustrated an set expansion approach that extracts instances from Web search queries given a set of input seed instances, which is similar in flavor to our system SEAL but different from the task addressed in this paper: the user provides no seeds, but instead provides the name of the set being expanded.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic set instance extraction using the web

Wang

Cohen

2009

Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural

View full text Add to dashboard Cite

An important and well-studied problem is the production of semantic lexicons from a large corpus. In this paper, we present a system named ASIE (Automatic Set Instance Extractor), which takes in the name of a semantic class as input (e.g., fruits) and automatically outputs its instances (e.g., orange, apple). ASIE is based on recent advances in set expansion -the problem of finding all instances of a set given a small number of "seed" instances. This approach effectively exploits web resources and can be easily adapted to different languages. In brief, our approach works by using simple language-dependent hyponym patterns to find a noisy set of initial seeds, and then using a state-of-the-art language-independent set expansion system to extend and rank these initial seeds. The proposed approach matches or outperforms prior systems on three English-language benchmarks. It also shows excellent performance on three dozen additional benchmark problems in English, Chinese and Japanese.

show abstract

“…We also compare our results to Pasca [7] as shown in Table 3. There are 197 countries in his evaluation dataset, and the input to his system is a set of seed named entities.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic set instance extraction using the web

Wang

Cohen

2009

Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural

View full text Add to dashboard Cite

show abstract

“…Over the past few years, many algorithms have been published on automatically harvesting terms and their conceptual types from the web and/or other large corpora (Etzioni et al, 2005;Pasca, 2007;Banko et al, 2007;Yi and Niblack, 2005;Snow et al, 2005). But several basic problems limit the eventual utility of the results.…”

Section: Collecting Information With Carementioning

confidence: 99%

“…Unlike (Etzioni et al, 2005), (Pasca, 2007) and (Snow et al, 2005), we learn both instances and concepts simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Toward completeness in concept extraction and classification

Hovy

Kozareva

Riloff

2009

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP '09

View full text Add to dashboard Cite

Many algorithms extract terms from text together with some kind of taxonomic classification (is-a) link. However, the general approaches used today, and specifically the methods of evaluating results, exhibit serious shortcomings. Harvesting without focusing on a specific conceptual area may deliver large numbers of terms, but they are scattered over an immense concept space, making Recall judgments impossible. Regarding Precision, simply judging the correctness of terms and their individual classification links may provide high scores, but this doesn't help with the eventual assembly of terms into a single coherent taxonomy. Furthermore, since there is no correct and complete gold standard to measure against, most work invents some ad hoc evaluation measure. We present an algorithm that is more precise and complete than previous ones for identifying from web text just those concepts 'below' a given seed term. Comparing the results to WordNet, we find that the algorithm misses terms, but also that it learns many new terms not in WordNet, and that it classifies them in ways acceptable to humans but different from WordNet.

show abstract

“…finding that population, flag or president are attributes for Country). The same author also provides a method to find named-entities (Paşca, 2007b) which is related to (Sekine and Suzuki, 2007) and (Komachi and Suzuki, 2008). None of these works, however, are related to our approach because they do not generate term taxonomies.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Taxonomy Extraction by Mining Topical Query Sessions

Fernández-Fernández¹,

Gayo-Avello

2009

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

View full text Add to dashboard Cite

Abstract:Search engine logs store detailed information on Web users interactions. Thus, as more and more people use search engines on a daily basis, important trails of users common knowledge are being recorded in those files. Previous research has shown that it is possible to extract concept taxonomies from full text documents, while other scholars have proposed methods to obtain similar queries from query logs. We propose a mixture of both lines of research, that is, mining query logs not to find related queries nor query hierarchies but actual term taxonomies. In this first approach we have researched the feasibility of finding hyponymy relations between terms or noun-phrases by exploiting specialization search patterns in topical sessions, obtaining encouraging preliminary results.

show abstract

Weakly-supervised discovery of named entities using web search queries

Cited by 84 publications

References 14 publications

Automatic set instance extraction using the web

Automatic set instance extraction using the web

Toward completeness in concept extraction and classification

Hierarchical Taxonomy Extraction by Mining Topical Query Sessions

Contact Info

Product

Resources

About