nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Passage Retrieval on Structured Documents Using Graph Attention Networks

verfasst von : Lucas Albarede, Philippe Mulhem, Lorraine Goeuriot, Claude Le Pape-Gardeux, Sylvain Marie, Trinidad Chardin-Segui

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Passage Retrieval systems aim at retrieving and ranking small text units according to their estimated relevance to a query. A usual practice is to consider the context a passage appears in (its containing document, neighbour passages, etc.) to improve its relevance estimation. In this work, we study the use of Graph Attention Networks (GATs), a graph node embedding method, to perform passage contextualization. More precisely, we first propose a document graph representation based on several inter- and intra-document relations. Then, we investigate two ways of leveraging the use of GATs on this representation in order to incorporate contextual information for passage retrieval. We evaluate our approach on a Passage Retrieval task for structured documents: CLEF-IP2013. Our results show that our document graph representation coupled with the expressive power of GATs allows for a better context representation leading to improved performances.

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 Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization

Nächstes Kapitel Expert Finding in Legal Community Question Answering

Albarede, L., Mulhem, P., Goeuriot, L., Le Pape-Gardeux, C., Marie, S., Chardin-Segui, T.: Passage retrieval in context: experiments on patents. In: Proceedings of CORIA 2021, Grenoble, France (2021). https://hal.archives-ouvertes.fr/hal-03230421

Andersson, L., Lupu, M., Palotti, J.A., Hanbury, A., Rauber, A.: When is the time ripe for natural language processing for patent passage retrieval? In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016, pp. 1453–1462. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2983323.2983858

Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate (2016)

Beigbeder, M.: Focused retrieval with proximity scoring. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, pp. 1755–1759. Association for Computing Machinery, New York (2010). https://doi.org/10.1145/1774088.1774462

Bendersky, M., Kurland, O.: Utilizing passage-based language models for document retrieval. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 162–174. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78646-7_17CrossRef

Callan, J.P.: Passage-level evidence in document retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1994, pp. 302–310. Springer-Verlag, Heidelberg (1994). https://doi.org/10.1007/978-1-4471-2099-5_31

Fernández, R., Losada, D., Azzopardi, L.: Extending the language modeling framework for sentence retrieval to include local context. Inf. Retr. 14, 355–389 (2011). https://doi.org/10.1007/s10791-010-9146-4CrossRef

Geva, S., Kamps, J., Lethonen, M., Schenkel, R., Thom, J.A., Trotman, A.: Overview of the INEX 2009 ad hoc track. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2009. LNCS, vol. 6203, pp. 4–25. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14556-8_4CrossRef

Gobeill, J., Ruch, P.: Bitem site report for the claims to passage task in CLEF-IP 2012. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy, 17–20 September 2012, CEUR Workshop Proceedings, vol. 1178. CEUR-WS.org (2012). http://ceur-ws.org/Vol-1178/CLEF2012wn-CLEFIP-GobeillEt2012.pdf

10.

Guo, J., et al.: A deep look into neural ranking models for information retrieval. Inf. Process. Manag. 57(6), 102067 (2020)

11.

Han, F., Niu, D., Lai, K., Guo, W., He, Y., Xu, Y.: Inferring search queries from web documents via a graph-augmented sequence to attention network. In: The World Wide Web Conference, WWW 2019, pp. 2792–2798. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3308558.3313746

12.

Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering (2020)

13.

Khattab, O., Zaharia, M.: Colbert: efficient and effective passage search via contextualized late interaction over BERT. CoRR abs/2004.12832 (2020). https://arxiv.org/abs/2004.12832

14.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)

15.

Krikon, E., Kurland, O., Bendersky, M.: Utilizing inter-passage and inter-document similarities for reranking search results. ACM Trans. Inf. Syst. 29(1) (2011). https://doi.org/10.1145/1877766.1877769

16.

Li, X., et al.: Learning better representations for neural information retrieval with graph information. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM 2020, pp. 795–804. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3340531.3411957

17.

Macdonald, C., McCreadie, R., Santos, R.L., Ounis, I.: From puppy to maturity: experiences in developing terrier. In: Proceedings of OSIR at SIGIR, pp. 60–63 (2012)

18.

Macdonald, C., Tonellotto, N., Ounis, I.: On single and multiple representations in dense passage retrieval. CoRR abs/2108.06279 (2021). https://arxiv.org/abs/2108.06279

19.

Mahdabi, P., Gerani, S., Huang, J.X., Crestani, F.: Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 113–122. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2484028.2484056

20.

Mahdabi, P., Keikha, M., Gerani, S., Landoni, M., Crestani, F.: Building queries for prior-art search. In: Hanbury, A., Rauber, A., de Vries, A.P. (eds.) IRFC 2011. LNCS, vol. 6653, pp. 3–15. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21353-3_2CrossRef

21.

Murdock, V., Croft, W.B.: A translation model for sentence retrieval. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 684–691. Association for Computational Linguistics, Vancouver (2005). https://www.aclweb.org/anthology/H05-1086

22.

Nguyen, T., et al.: MS MARCO: a human generated machine reading comprehension dataset. CoRR abs/1611.09268 (2016). http://arxiv.org/abs/1611.09268

23.

Norozi, M.A., Arvola, P.: Kinship contextualization: Utilizing the preceding and following structural elements. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 837–840. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2484028.2484111

24.

Norozi, M.A., Arvola, P., de Vries, A.P.: Contextualization using hyperlinks and internal hierarchical structure of wikipedia documents. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, pp. 734–743. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2396761.2396855

25.

Norozi, M.A., de Vries, A.P., Arvola, P.: Contextualization from the bibliographic structure (2012)

26.

Piroi, F., Lupu, M., Hanbury, A.: Overview of CLEF-IP 2013 lab. In: Forner, P., Müller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 232–249. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40802-1_25CrossRef

27.

Robertson, S., Walker, S., Jones, S., Hancock-Beaulieu, M., Gatford, M.: Okapi at trec-3, pp. 109–126 (1996)

28.

Sheetrit, E., Shtok, A., Kurland, O.: A passage-based approach to learning to rank documents (2019)

29.

Vaswani, A., et al.: Attention is all you need (2017)

30.

Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks (2018)

31.

Wang, X., et al.: Heterogeneous graph attention network. CoRR abs/1903.07293 (2019). http://arxiv.org/abs/1903.07293

32.

Xiong, L., et al.: Approximate nearest neighbor negative contrastive learning for dense text retrieval (2020)

33.

Xue, X., Croft, W.B.: Automatic query generation for patent search. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 2037–2040. Association for Computing Machinery, New York (2009). https://doi.org/10.1145/1645953.1646295

34.

Yu, J., et al.: Modeling text with graph convolutional network for cross-modal information retrieval (2018)

35.

Zhang, T., Liu, B., Niu, D., Lai, K., Xu, Y.: Multiresolution graph attention networks for relevance matching. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (2018). https://doi.org/10.1145/3269206.3271806

Titel: Passage Retrieval on Structured Documents Using Graph Attention Networks
verfasst von: Lucas Albarede
Philippe Mulhem
Lorraine Goeuriot
Claude Le Pape-Gardeux
Sylvain Marie
Trinidad Chardin-Segui
Verlag: Springer International Publishing
Buch: Advances in Information Retrieval
Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-99739-7_2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"