Survey and evaluation of web search engine hit counts as research tools in computational linguistics

Information Systems

Volumn 73, Issue , 2018, Pages 50-60

  Sánchez, David ^a   Martínez Sanahuja, Laura ^a   Batet, Montserrat ^b  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^b UNIVERSITAT OBERTA DE CATALUNYA   (Spain)

Author keywords

Computational linguistics; Hit counts; Information distribution; Semantic similarity; Web search engines

Indexed keywords

COMPUTATIONAL LINGUISTICS; DATA MINING; DIGITAL STORAGE; INFORMATION RETRIEVAL; LINGUISTICS; NATURAL LANGUAGE PROCESSING SYSTEMS; QUERY LANGUAGES; SEMANTICS; SURVEYS; WEBSITES;

GENERAL-PURPOSE SEARCH ENGINES; HIT COUNT; INFORMATION DISTRIBUTIONS; LINGUISTIC ANALYSIS; RESEARCH TOOLS; SEMANTIC SIMILARITY; TEMPORAL CONSISTENCY; TEXTUAL DATA;

SEARCH ENGINES;

EID: 85040055325     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2017.12.007     Document Type: Article

References (62)

1. Resnik, P., Smith, N., The web as a parallel corpus. Comput. Ling. 29 (2003), 349–380.
2. Özacar, T., A tool for producing structured interoperable data from product features on the web. Inf. Syst. 56 (2016), 36–54.
3. Yerva, S.R., Miklós, Z., Aberer, K., Quality-aware similarity assessment for entity matching in web data. Inf. Syst. 37 (2012), 336–351.
4. Bollegala, D., Matsuo, Y., Ishizuka, M., Measuring semantic similarity between words using web search engines. Proc. of 16th international conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, 2007, 757–766.
5. Chow, R., Golle, P., Staddon, J., Detecting privacy leacks using corpus-based association rules. Proc. of 14th Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, 2008, 893–901.
6. Cilibrasi, R.L., Vitányi, P.M.B., The google similarity distance. IEEE Trans. Knowl. Data Eng. 19 (2006), 370–383.
7. Lapata, M., Keller, F., Web-based models for natural language processing. ACM Trans. Speech Lang. Process. 2 (2005), 1–31.
8. Thelwall, M., Extracting accurate and complete results from search engines: case study windows live. J. Am. Soc. Inf. Sci. Technol. 59 (2007), 38–50.
9. Grefenstette, G., The WWW as a resource for example-based MT tasks. Proc. of ASLIB Conference on Translating and the Computer 21, London, U.K., 1999.
10. Turney, P.D., Mining the web for synonyms: PMI-IR versus LSA on TOEFL. Proc. of 12th European Conference on Machine Learning, ECML 2001, Freiburg, Germany, 2001, 491–502.
11. Keller, F., Lapata, M., Using the web to obtain frequencies for unseen bigrams. Comput. Ling. 29 (2003), 459–484.
12. Nakov, P., Hearst, M., Search engine statistics beyond the n-gram: application to noun compound bracketing. Proc. of Ninth Conference on Computational Natural Language Learning, Ann Arbor, Michigan, US, 2005, 17–24.
13. Etzioni, O., Cafarella, M., Downey, D., Popescu, A., Shaked, T., Soderland, S., Weld, D., Yates, A., Unsupervised named-entity extraction form the web: an experimental study. Artif. Intell. 165 (2005), 91–134.
14. Sánchez, D., Isern, D., Automatic extraction of acronym definitions from the web. Appl. Intell. 34 (2011), 311–327.
15. Sánchez, D., Moreno, A., Learning non-taxonomic relationships from web documents for domain ontology construction. Data Knowl. Eng. 63 (2008), 600–623.
16. Sánchez, D., Moreno, A., Pattern-based automatic taxonomy learning from the web. AI Commun. 21 (2008), 27–48.
17. Sánchez, D., A methodology to learn ontological attributes from the web. Data Knowl. Eng. 69 (2010), 573–597.
18. Sánchez, D., Moreno, A., Vasto-Terrientes, L.D., Learning relation axioms from text: an automatic web-based approach. Expert Syst. Appl. 39 (2012), 5792–5805.
19. Sánchez, D., Batet, M., Valls, A., Gibert, K., Ontology-driven web-based semantic similarity. J. Intell. Inf. Syst. 35 (2010), 383–413.
20. Cimiano, P., Handschuh, S., Staab, S., Towards the self-annotating web. Proc. of 13th international conference on World Wide Web, WWW 2004, New York, USA, 2004, 462–471.
21. Sánchez, D., Isern, D., Millán, M., Content annotation for the semantic web: an automatic web-based approach. Knowl. Inf. Syst. 27 (2011), 393–418.
22. Viejo, A., Sánchez, D., Castellà-Roca, J., Preventing automatic user profiling in web 2.0 applications. Knowl.-Based Syst. 36 (2012), 191–205.
23. Matsuo, Y., Mori, J., Hamasaki, M., Nishimura, T., Takeda, H., Hasida, K., Ishizuka, M., Polyphonet: an advanced social network extraction system from the web. Web Semant. 5 (2007), 262–278.
24. Sánchez, D., Batet, M., C-sanitized: a privacy model for document redaction and sanitization. J. Assoc. Inf. Sci. Technol. 67 (2016), 148–163.
25. Björneborn, L., Ingwersen, P., Toward a basic framework for webometrics. J. Am. Soc. Inf. Sci. Technol. 55 (2004), 1216–1227.
26. Chapelle, O., Chang, Y., Yahoo! learning to rank challange overview. Proc. of Yahoo! Learning to Rank Challenge at ICML 2010, Haifa, Israel, 2011, 1–24.
27. Lewandowski, D., Evaluating the retrieval effectiveness of web search engines using a representative query sample. J. Assoc. Inf. Sci. Technol. 66 (2015), 1763–1775.
28. Macfarlane, A., Evaluation of web search for the information practitioner. Aslib Proc. 59 (2007), 352–366.
29. Deka, S.K., Lahkar, N., Performance evaluation and comparison of the five most used search engines in retrieving web resources. Online Inf. Rev. 34 (2010), 757–771.
30. Bilal, D., Ranking, relevance judgment, and precision of information retrieval on children's queries: evaluation of Google, Yahoo!, Bing, Yahoo! Kids, and ask Kids. J. Am. Soc. Inf. Sci. Technol. 63 (2012), 1879–1896.
31. Zhang, J., Fei, W., Search engines? responses to several search feature selections. Int. Inf. Library Rev. 42 (2010), 212–225.
32. Bar-Ilan, J., Levene, M., A method to assess search engine results. Online Inf. Rev. 35 (2011), 854–868.
33. Broder, A., A taxonomy of web search. ACM Sigir forum 36 (2002), 3–10.
34. Thelwall, M., Quantitative comparisons of search engine results. J. Am. Soc. Inf. Sci. Technol. 59 (2008), 1702–1710.
35. Uyar, A., Investigation of the accuracy of search engine hit counts. J. Inf. Sci 35 (2009), 469–480.
36. Satoh, K., Yamana, H., Hit count reliability: how much can we trust hit counts?. Proc. of 14th Asia-Pacific international conference on Web Technologies and Applications, 2012, 751–758.
37. Funahashi, T., Yamana, H., Reliability verification of search engines’ hit counts: how to select a reliable hit count for a query. Current Trends in Web Engineering, 2010, Springer, 114–125.
38. Tian, T., Geller, J., Chun, S.A., Predicting web search hit counts. Proc. of 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Toronto, ON, Canada, 2010, 162–166.
39. Tian, T., Chun, S.A., Geller, J., A prediction model for web search hit counts using word frequencies. J. Inf. Sci. 37 (2011), 462–475.
40. Martínez-Sanahuja, L., Sánchez, D., Evaluating the suitability of web search engines as proxies for knowledge discovery from the web. Proc. of 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, York, UK, 2016, 169–178.
41. Netmarketshare. Desktop Search Engine Market Share. March 2017. Available at https://www.netmarketshare.com/search-engine-market-share.aspx?qprid=4&qpcustomd=0.
42. Kilgarriff, A., Googleology is bad science. Comput. Ling. 33 (2007), 147–151.
43. Rubenstein, H., Goodenough, J., Contextual correlates of synonymy. Commun. ACM 8 (1965), 627–633.
44. Snyder, H., Rosenbaum, H., Can search engines be used as tools for web-link analysis? A critical view. J. Doc. 55 (1999), 375–384.
45. Mettrop, W., Nieuwenhuysen, P., Internet search engines - fluctuations in document accessibility. J. Doc. 57 (2001), 623–651.
46. Zhang, J., Long, X., Suel, T., Performance of compressed inverted list caching in search engines. Proc. of 17th international conference on World Wide Web Beijing, China, 2008, 387–396.
47. E. Davis. A difference of a factor of 70,000 between hit counts and results returned in Google. In: Unpublished technical note; 2015.
48. Dean, J., Challenges in building large-scale information retrieval systems. 2009 Conference on Web Search and Data Mining, 2009.
49. Skobeltsyn, G., Junqueira, F., Plachouras, V., Baeza-Yates, R., ResIn: a combination of results caching and index pruning for high-performance web search engines. Proc. of 31st annual international ACM SIGIR conference on Research and development in information retrieval, Singapore, Singapore, 2008, 131–138.
50. Bar-Ilan, J., Levene, M., Mat-Hassan, M., Methods for evaluating dynamic changes in search engine rankings: a case study. J. Doc. 62 (2006), 708–729.
51. Batet, M., Sánchez, D., Review on Semantic Similarity. Encyclopedia of Information Science and Technology, 3rd, 2014, IGI Global, 7575–7583.
52. Lastra-Díaz, JuanJ., García-Serrano, A., Batet, M., Fernández, M., Chirigati, F., HESML: a scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset. Inf. Syst. 66 (2017), 97–118.
53. Batet, M., Ontology based semantic clustering. AI Commun. 24 (2011), 291–292.
54. McInnes, B.T., Pedersen, T., Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text. J. Biomed. Inf. 46 (2013), 1116–1124.
55. Martínez, S., Sánchez, D., Valls, A., A semantic framework to protect the privacy of electronic health records with non-numerical attributes. J. Biomed. Inf. 46 (2013), 294–303.
56. Sahlgren, M., The distributional hypothesis. Rivista di Linguistica 20 (2008), 33–53.
57. Bouma, G., Normalized (Pointwise) mutual information in collocation extraction. Proc. of Biennial GSCL Conference 2009, Tübingen, Germany, 2009, 31–40.
58. Mohammad, S., Hirst, G., Distributional measures of semantic distance: a survey. http://arxiv.org/abs/1203.1858, 2006.
59. Bollegala, D., Matsuo, Y., Ishizuka, M., A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web. Proc. of Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Singapore, Republic of Singapore, 2009, 803–812.
60. van den Bosch, A., Bogers, T., de Kunder, M., Estimating search engine index size variability: a 9-year longitudinal study. Scientometrics 107 (2016), 839–856.
61. Pedersen, T., Pakhomov, S., Patwardhan, S., Chute, C., Measures of semantic similarity and relatedness in the biomedical domain. J. Biomed. Inf. 40 (2007), 288–299.
62. Zhang, S., The pitfalls of using Google ngram to study language. in: Wired, 2015.