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Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; EMNLP 2021 ; https://hal.archives-ouvertes.fr/hal-03480371 ; EMNLP 2021, Nov 2021, Punta Cana (online), Dominican Republic. pp.10438-10449 (2021)
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QuestEval: Summarization Asks for Fact-based Evaluation
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.sorbonne-universite.fr/hal-03541895 ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Nov 2021, Punta Cana (en ligne), Dominican Republic. pp.6594-6604, ⟨10.18653/v1/2021.emnlp-main.529⟩ ; https://2021.emnlp.org/ (2021)
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Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction ...
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Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction ...
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The Rare Word Issue in Natural Language Generation: A Character-Based Solution
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
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In: ECIR 2020 - 42nd European Conference on Information Retrieval ; https://hal.archives-ouvertes.fr/hal-02503463 ; ECIR 2020 - 42nd European Conference on Information Retrieval, Apr 2020, Lisbon, Portugal (2020)
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Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction!
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.archives-ouvertes.fr/hal-03480380 ; Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020, Punta Cana (Online), Dominican Republic. pp.3689-3701, ⟨10.18653/v1/2020.emnlp-main.301⟩ ; https://aclanthology.org/2020.emnlp-main.301 (2020)
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization ...
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
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Incorporating Visual Semantics into Sentence Representations within a Grounded Space
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In: 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing ; https://hal.sorbonne-universite.fr/hal-02351003 ; 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Nov 2019, Hong Kong, China. pp.696-707, ⟨10.18653/v1/D19-1064⟩ (2019)
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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.sorbonne-universite.fr/hal-01156600 ; BMC Bioinformatics, BioMed Central, 2015, 16 (1), pp.138. ⟨10.1186/s12859-015-0564-6⟩ (2015)
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| 12 |
Coping with the Document Frequency Bias in Sentiment Classification
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 6 No. 1 (2012): Sixth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2012)
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Abstract:
In this article, we study the polarity detection problem using linear supervised classifiers. We show the interest of penalizing the document frequencies in the regularization process to increase the accuracy. We propose a systematic comparison of different loss and regularization functions on this particular task using the Amazon dataset. Then, we evaluate our models according to three criteria: accuracy, sparsity and subjectivity. The subjectivity is measured by projecting our dictionary and optimized weight vector on the SentiWordNet lexicon. This original approach highlights a bias in the selection of the relevant terms during the regularization procedure: frequent terms are overweighted compared to their intrinsic subjectivities.We show that this bias appears whatever the chosen loss or regularization and on all datasets: it is closely link to the gradient descent technique. Penalizing the document frequency during the learning step enables us to improve significantly our performances. A lot of sentimental markers appear rarely and thus, are unappreciated by statistical learning algorithms. Explicitly boosting their influences leads to increasing the accuracy in the sentiment classification task.
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Keyword:
Document Frequency Penalization; Elastic Net Regularization; Sentiment Classification
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URL: https://ojs.aaai.org/index.php/ICWSM/article/view/14244
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Multiview Clustering of Multilingual Documents
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In: Proceedings of the 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; https://hal.archives-ouvertes.fr/hal-01292100 ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010), Jul 2010, Geneva, Switzerland. pp.812-822, ⟨10.1145/1835449.1835633⟩ (2010)
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Improving Image Annotation in Imbalanced Classification Problems with Ranking SVM
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In: Multilingual Information Access Evaluation II. Multimedia Experiments ; CLEF 2009 - 10th Workshop of the Cross-Language Evaluation Forum ; https://hal.archives-ouvertes.fr/hal-00581661 ; CLEF 2009 - 10th Workshop of the Cross-Language Evaluation Forum, Sep 2009, Corfu, Greece. pp.291-294, ⟨10.1007/978-3-642-15751-6_37⟩ (2009)
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A Comparative Study of Diversity Methods for Hybrid Text and Image Retrieval Approaches
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In: Evaluating Systems for Multilingual and Multimodal Information Access ; 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008 ; https://hal.archives-ouvertes.fr/hal-00581664 ; 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Sep 2008, Aarhus, Denmark. pp.585-592, ⟨10.1007/978-3-642-04447-2_72⟩ (2008)
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The XML Wikipedia Corpus
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In: ISSN: 0163-5840 ; Sigir Forum ; https://hal.archives-ouvertes.fr/hal-01172244 ; Sigir Forum, Association for Computing Machinery (ACM), 2006, 40 (1), pp.64-69. ⟨10.1145/1147197.1147210⟩ (2006)
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A Machine Learning based Approach to Evaluating Retrieval Systems
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In: Proc. Human Language Technology Conference - HLT-NAACL'06 ; https://hal.archives-ouvertes.fr/hal-01352077 ; Proc. Human Language Technology Conference - HLT-NAACL'06, Jun 2006, New-York, United States. pp.399-406, ⟨10.3115/1220835.1220886⟩ (2006)
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Structured Multimedia Document Classification
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In: ACM Document Engeneering ; https://hal.archives-ouvertes.fr/hal-01357593 ; ACM Document Engeneering, Nov 2003, Grenoble, France. pp.153-160, ⟨10.1145/958220.958249⟩ (2003)
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