Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Use of Blockchain for Smart T-Shirt Design Ownership Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Temporal Information Retrieval and Its Application: A Survey

verfasst von : Rakshita Bansal, Monika Rani, Harish Kumar, Sakshi Kaushal

Erschienen in: Emerging Research in Computing, Information, Communication and Applications

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With an advent of the Web, a tremendous amount of information is available online. Information can be organized and explored in the time dimension. This temporal information has to be distilled out, so as to extract the temporal entities such as temporal expressions and temporal relations out of it. Temporal information processing is an ongoing field of research that deals with natural language text, temporal relations, events or temporal queries. This paper presents a detailed analysis of the work carried out under temporal information retrieval (TIR) highlighting its subtasks like information extraction, indexing, ranking, query processing, clustering and classification. Also, it presents various challenges while dealing with temporal information. To the end, various application areas are elaborated such as temporal summarization, exploration and future event retrieval.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Descriptive Data Analysis of Real Estate Using Cube Technology
Nächstes Kapitel A Survey on Multi-resolution Methods for De-noising Medical Images
Literatur
1.
2.
3.
Ingria, R., Saur, R., Pustejovsky, J., et al. (2002). TimeML: Robust specification of event and temporal expressions in text. In Proceedings of the AAAI Spring Symposium on New Directions in Question Answering (pp. 28–34).
4.
5.
Pustejovsky, J., Lee, K., Bunt, H., & Romary, L. (2010). ISO-TimeML: An international standard for semantic annotation. In LREC, May 2010, La Valette, Malta.
6.
Mani, I., & Wilson, G. (2000). Robust temporal processing of news. In Association for Computational Linguistics (pp. 69–76).
7.
Negri, M., & Marseglia, L. (2005). Recognition and normalization of time expressions: ITC-irst at TERN 2004 (Tech. Report WP3.7, Information Society Technologies).
8.
Uzzaman, N., & Allen, J. F. (2010). TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text. In Proceedings of the 5th International Workshop on Semantic Evaluation ACL (pp. 276–283).
9.
Chang, A. X., & Manning, C. D. (2012). SUTime: A library for recognizing and normalizing time expressions. In LREC (pp. 3735–3740).
10.
Strötgen, J., & Gertz, M. (2010). HeidelTime high-quality rule-based extraction and normalization of temporal expressions. In Proceedings of the 5th International Workshop on Semantic Evaluation, ACL (pp. 321–324).
11.
12.
13.
Bethard, S. (2013). ClearTK-TimeML: A minimalist approach to TempEval 2013. Seventh International Workshop on Semantic Evaluation, 2, 10–14.
14.
Llorens, H., Saquete, E., & Navarro, B. (2010). Tipsem (English and Spanish): Evaluating crfs and semantic roles in tempeval-2. In Proceeding SemEval’10 Proceedings of the 5th International Workshop on Semantic Evaluation (pp. 284–291).
15.
Tang, Y., Ye, X., & Tang, N. (2011). Temporal information processing technology and its application, Chap. 8, (pp. 51–158).
16.
17.
Berberich, K., Bedathur, S., Neumann, T., & Weikum, G. (2007). A time machine for text search. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval —SIGIR’07, (pp. 519–526). https://​doi.​org/​10.​1145/​1277741.​1277831.
18.
Berberich, K., Bedathur, S., Neumann, T., & Weikum, G. (2007). FluxCapacitor: Efficient time-travel text search. In Proceedings of the VLDB ’07 (pp. 1414–1417).
19.
20.
21.
Anand, A., Bedathur, S., Berberich, K., & Schenkel, R. (2012). Index maintenance for time-travel text search. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR’12 (pp. 235–243). https://​doi.​org/​10.​1145/​2348283.​2348318.
22.
23.
Li, X., & Croft, W. B. (2003). Time-based language models. In Proceedings of the 12th International Conference on Information and Knowledge Management (pp. 469–475).
24.
Jatowt, A., Kawai, Y., & Tanaka, K. (2005). Temporal ranking of search engine results. In Proceedings of the 6th International Conference on Web Information Systems Engineering (pp. 43–52).
25.
Zhang, R., Chang, Y., & Zheng, Z. (2009). Search result re-ranking by feedback control adjustment for time-sensitive query. In Proceedings of NAACL HLT (pp. 165–168).
26.
Strötgen, J., & Gertz, M. (2013). Proximity 2-aware ranking for textual, temporal, and geographic queries. In Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management—CIKM’13 (pp. 739–744).
27.
Costa, M., Couto, F., & Silva, M. (2014). Learning temporal-dependent ranking models. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval—SIGIR’14 (pp. 757–766).
28.
Cheng, S., Arvanitis, A., & Hristidis, V. (2013). How fresh do you want your search results? In Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management—CIKM’13 (pp. 1271–1280). https://​doi.​org/​10.​1145/​2505515.​2505696.
29.
30.
31.
Dakka, W., Gravano, L., & Ipeirotis, P. (2012). Answering general time-sensitive queries. IEEE Transactions on Knowledge and Data Engineering, 24(2), 220–235.CrossRef
32.
33.
Alonso, O., & Gertz, M. (2006). Clustering of search results using temporal attributes. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR’06 (pp. 597–598). https://​doi.​org/​10.​1145/​1148170.​1148273.
34.
35.
36.
Jatowt, A., & Au Yeung, C. (2011). Extracting collective expectations about the future from large text collections. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management—CIKM’11 (pp. 1259–1264). https://​doi.​org/​10.​1145/​2063576.​2063759.
37.
Mani, I., Verhagen, M., Wellner, B., et al. (2006). Machine learning of temporal relations. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL—ACL’06 (pp. 753–760). https://​doi.​org/​10.​3115/​1220175.​1220270.
38.
Chambers, N., Wang, S., & Jurafsky, D. (2007). Classifying temporal relations between events. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (ACL’07), Association for Computational Linguistics, Stroudsburg, PA, USA (pp. 173–176).
39.
UzZaman, N., & Allen, J. (2011). Temporal evaluation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 351–356).
40.
Souza, J. D., & Ng, V. (2013). Classifying temporal relations with rich linguistic knowledge. In Proceedings of NAACL HLT (pp. 918–927).
41.
Laokulrat, N., Miwa, M., Tsuruoka, Y., & Chikayama, T. (2013). Uttime: Temporal relation classification using deep syntactic features. In Proceedings of the Seventh International Workshop on Semantic Evaluation (pp. 88–92).
42.
43.
Campos, R., Dias, G., Jorge, A. M., & Nunes, C. (2014). C GTE-Cluster: A temporal search interface for implicit. In Proceedings of the European Conference on IR Research (pp. 775–779).
44.
45.
Xu, T., Mcnamee, P., & Oard, D. W. (2013). HLTCOE at TREC 2013: Temporal summarization. In Proceedings of the 22nd Text Retrieval Conference (TREC’13).
46.
47.
Wang, B., Liakata, M., Tsakalidis, A., Kolaitis, S. G., Papadopoulos, S., Apostolidis, L., et al. (2017). TOTEMSS: Topic-based, temporal sentiment summarisation for Twitter. In Proceedings of the IJCNLP, System Demonstrations (pp. 21–24).
48.
49.
50.
Zhang, S., Bahrampour, S., Ramakrishnan, N., Schott, L., & Shah, M. (2017). Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. In IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5970–5974).
Metadaten
Titel
Temporal Information Retrieval and Its Application: A Survey
verfasst von
Rakshita Bansal
Monika Rani
Harish Kumar
Sakshi Kaushal
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Emerging Research in Computing, Information, Communication and Applications

Print ISBN: 978-981-13-6000-8

Electronic ISBN: 978-981-13-6001-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-13-6001-5_19

Neuer Inhalt

    Bildnachweise