La femme du physiologiste (Policier) (French Edition)

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On our test corpus and using similar evaluation OpeNER averaged a precision and recall of 0. This may seem surprisingly low compared to the standards usually set by this tool, but actually is a good illustration of how difficult it may get to find the correct tagging in fictional texts. The two worst cases Les Malheurs de Sophie and Germinie Larcerteux are shown in details in Tables A1 and A2 Appendix A , and show that those problems are about as much related to bad classifications as missing entities.

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Our meta-classification methods perform on average better than that, and we can see an encouraging trend that the various strategies we tried out tended to get increasingly better results and to close the standard deviation gap. That being said, as far as SD is concerned and taken into account, this improvement was not statistically significative. Yet, the meta-recognition method managed a F 1 score no worse than 0. Table 4. Averaged precision, recall, and F 1 scores, for each classifier.

The contribution of this paper is to establish a set of efficient and autonomous tools that can be run in a limited environment such as a web server , on any French novel without the need of training or manual user input, yet keeping reliable results. We showed that combining different classifiers, especially using a meta-recognition technique, allowed to attain an overall better score than each of them would separately, and to outperform some state-of-the-art tools in the very narrow considered use case. Yet, we are aware this process implies several hard assumptions that may break under certain circumstances and cause the system to fail, like the systematic extraction limited to capitalized proper nouns.

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Future works may focus on coreference resolution by merging entities that refer to the same character, or conversely, disambiguation of homonyms, even if this does not happen very often in closed environments like fictional works. In parallel to a reliable extraction of the characters networks, further textual analytics may try to uncover the nature of the relations between them.

CB initiated this research, wrote the software components, and elaborated the test sets and methods described in this report. FK supervised this research and advised on the necessary tests and developments needed to improve its relevance. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

This research was made possible by a very precious and close collaboration with Daniel de Roulet, Swiss architect, computer scientist, and author. Albanese, A.

Small demons makes big splash at Frankfurt book fair. Publishers Weekly Google Scholar. Azpeitia, A. NERC-fr: supervised named entity recognition for French. In Text, Speech and Dialogue , Vol. Sojka, A. Pala, — Cham: Springer International Publishing. Dumais, S.

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Latent semantic analysis. Annual Review of Information Science and Technology — Fleiss, J. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and Psychological Measurement —9. Geno, M. Glass, K. Goh, H. Automatic identification of protagonist in fairy tales using verb. Hutchison, T. Kanade, J. Kittler, J. Kleinberg, F. Mattern, J. Mitchell, M. Naor, O.

Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M. Vardi, G. Weikum, P. Tan, S. Chawla, C. Ho, and J. Bailey, — Berlin, Heidelberg: Springer.

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Grevisse, M. Bruxelles: Duculot. Hirschberg, J. Advances in natural language processing. Science —6.


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Jovanovic, J. Automated semantic tagging of textual content.

5 FEMMES À L'ÉCOLE DE POLICE

IT Professional 38— Karsdorp, F. Moretti, F. Distant Reading. London, New York: Verso.


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Mosallam, Y. Unsupervised named entity recognition and disambiguation: an application to old French journals. In Industrial Conference on Data Mining , 12— Petersburg: Springer. Scheirer, W. Meta-recognition: the theory and practice of recognition score analysis. Si, L. The boil marker sector. The holder of this diploma is a specialist in boiler works, industrial pipe work and metallic structures.

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