| 1 |
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Mapping probability word problems to executable representations ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Interlocutors’ Age Impacts Teenagers’ Online Writing Style: Accommodation in Intra- and Intergenerational Online Conversations
|
|
|
|
In: Front Artif Intell (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
|
|
|
|
In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 9 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Neural Machine Translation of Artwork Titles Using Iconclass Codes ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
A deep generative approach to native language identification ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
What makes a distributional context useful? Lexical diversity is more important than frequency ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Children Probably Store Short Rather Than Frequent or Predictable Chunks: Quantitative Evidence From a Corpus Study
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection
|
|
Daelemans, Walter; Kestemont, Mike; Manjavacas, Enrique; Potthast, Martin; Rangel, Francisco; Rosso, Paolo; Specht, Günther; Stamatatos, Efstathios; Stein, Benno; Tschuggnall, Michael; Wiegmann, Matti; Zangerle, Eva. - : Springer-Verlag, 2019
|
|
Abstract:
[EN] We briefly report on the four shared tasks organized as part of the PAN 2019 evaluation lab on digital text forensics and authorship analysis. Each task is introduced, motivated, and the results obtained are presented. Altogether, the four tasks attracted 373 registrations, yielding 72 successful submissions. This, and the fact that we continue to invite the submission of software rather than its run output using the TIRA experimentation platform, demarcates a good start into the second decade of PAN evaluations labs. ; The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212- B-C31). Our special thanks goes to all PAN participants for providing high-quality submission, to Symanto (https://www.symanto.net) for sponsoring the PAN Lab 2019 and to The Logic Value (https://thelogicvalue.com) for sponsoring the author profiling shared task award. ; Daelemans, W.; Kestemont, M.; Manjavacas, E.; Potthast, M.; Rangel, F.; Rosso, P.; Specht, G. (2019). Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection. Lecture Notes in Computer Science. 11696:402-416. https://doi.org/10.1007/978-3-030-28577-7_30 ; S ; 402 ; 416 ; 11696 ; Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Sebastopol (2009) ; Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.): CLEF 2019 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, September 2019 ; Cardoso, J., Sousa, R.: Measuring the performance of ordinal classification. Int. J. Pattern Recognit Artif Intell. 25(08), 1173–1195 (2011) ; Hellekson, K., Busse, K. (eds.): The Fan Fiction Studies Reader. University of Iowa Press, Iowa City (2014) ; Juola, P.: Authorship attribution. Found. Trends Inf. Retrieval 1(3), 233–334 (2006) ; Kestemont, M., Stamatatos, E., Manjavacas, E., Daelemans, W., Potthast, M., Stein, B.: Overview of the cross-domain authorship attribution task at PAN 2019. In: Cappellato et al. [2] ; Kestemont, M., Stover, J.A., Koppel, M., Karsdorp, F., Daelemans, W.: Authenticating the writings of Julius Caesar. Expert Syst. Appl. 63, 86–96 (2016). https://doi.org/10.1016/j.eswa.2016.06.029 ; Kestemont, M., et al.: Overview of the author identification task at PAN-2018: cross-domain authorship attribution and style change detection. In: Cappellato, L. et al. (eds.) Working Notes Papers of the CLEF 2018 Evaluation Labs, Avignon, France, 10–14 September 2018, pp. 1–25 (2018) ; Koppel, M., Schler, J., Argamon, S.: Computational methods in authorship attribution. J. Am. Soc. Inf. Sci. Technol. 60(1), 9–26 (2009) ; Koppel, M., Winter, Y.: Determining if two documents are written by the same author. J. Assoc. Inf. Sci. Technol. 65(1), 178–187 (2014) ; Júnior, P.R.M., et al.: Nearest neighbors distance ratio open-set classifier. Mach. Learn. 106(3), 359–386 (2017) ; Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of Workshop at International Conference on Learning Representations (ICLR 2013) (2013) ; Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013) ; Oliphant, T.: NumPy: A Guide to NumPy. Trelgol Publishing (2006). http://www.numpy.org/ ; Pedregos, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011) ; Pizarro, J.: Using n-grams to detect bots on Twitter: notebook for PAN at CLEF 2019. In: Cappellato et al. [2] ; Potthast, M., et al.: Who wrote the web? Revisiting influential author identification research applicable to information retrieval. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 393–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30671-1_29 ; Potthast, M., Gollub, T., Wiegmann, M., Stein, B.: TIRA integrated research architecture. In: Ferro, N., Peters, C. (eds.) Information Retrieval Evaluation in a Changing World - Lessons Learned from 20 Years of CLEF. Springer, Heidelberg (2019) ; Potthast, M., Rosso, P., Stamatatos, E., Stein, B.: A decade of shared tasks in digital text forensics at PAN. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11438, pp. 291–300. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15719-7_39 ; Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, 8–11 September, Toulouse, France. CEUR-WS.org (2015) ; Rangel, F., Rosso, P.: On the implications of the general data protection regulation on the organisation of evaluation tasks. Lang. Law= Linguagem e Direito 5(2), 95–117 (2018) ; Rangel, F., Rosso, P.: Overview of the 7th author profiling task at PAN 2019: bots and gender profiling. In: Cappellato et al. [2] ; Rangel, F., et al.: Overview of the 2nd author profiling task at PAN 2014. In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) CLEF 2014 Evaluation Labs and Workshop - Working Notes Papers, 15–18 September, Sheffield, UK. CEUR-WS.org (2014) ; Rangel, F., Franco-Salvador, M., Rosso, P.: A low dimensionality representation for language variety identification. In: Gelbukh, A. (ed.) CICLing 2016. LNCS, vol. 9624, pp. 156–169. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75487-1_13 ; Rangel, F., Rosso, P., G’omez, M.M., Potthast, M., Stein, B.: Overview of the 6th author profiling task at PAN 2018: multimodal gender identification in Twitter. In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org (2017) ; Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013. In: Forner, P., Navigli, R., Tufis, D. (eds.) CLEF 2013 Evaluation Labs and Workshop - Working Notes Papers, 23–26 September, Valencia, Spain, September 2013 ; Rangel, F., Rosso, P., Potthast, M., Stein, B.: Overview of the 5th author profiling task at PAN 2017: gender and language variety identification in Twitter. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017 ; Rangel, F., Rosso, P., Verhoeven, B., Daelemans, W., Potthast, M., Stein, B.: Overview of the 4th author profiling task at PAN 2016: cross-genre evaluations. In: Balog, K., Cappellato, L., Ferro, N., Macdonald, C. (eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org., September 2016 ; Rosso, P., Rangel, F., Potthast, M., Stamatatos, E., Tschuggnall, M., Stein, B.: Overview of PAN’16: new challenges for authorship analysis: cross-genre profiling, clustering, diarization, and obfuscation. In: Fuhr, N., et al. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 332–350. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44564-9_28 ; Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002) ; Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60, 538–556 (2009) ; Teahan, W.J., Harper, D.J.: Using compression-based language models for text categorization. In: Croft, W.B., Lafferty, J. (eds.) Language Modeling for Information Retrieval. INRE, vol. 13, pp. 141–165. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-017-0171-6_7 ; Tschuggnall, M., et al.: Overview of the author identification task at PAN-2017: style breach detection and author clustering. In: Cappellato, L. et al. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs, pp. 1–22 (2017) ; Wiegmann, M., Stein, B., Potthast, M.: Celebrity profiling. In: 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Association for Computational Linguistics, July 2019 ; Wiegmann, M., Stein, B., Potthast, M.: Overview of the celebrity profiling task at PAN 2019. In: Cappellato et al. [2] ; Zangerle, E., Tschuggnall, M., Specht, G., Potthast, M., Stein, B.: Overview of the style change detection task at PAN 2019. In: Cappellato et al. [2]
|
|
Keyword:
Bots and gender profiling; Celebrity profiling; Cross-domain authorship attribution; LENGUAJES Y SISTEMAS INFORMATICOS; PAN; Style change detection
|
|
URL: http://hdl.handle.net/10251/159144
https://doi.org/10.1007/978-3-030-28577-7_30
|
|
BASE
|
|
Hide details
|
|
|
| 17 |
Multilingual Cross-domain Perspectives on Online Hate Speech ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Patient representation learning and interpretable evaluation using clinical notes ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
