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Schedule
Content
Examination
Assignments
Reading

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Language Technology: Research and Development

Note that this page has been migrated from a previous server. There is thus a risk that not all links work correctly

Credits: 15 hp
Syllabus: 5LN714
Teachers: Sara Stymne, Ali Basirat, Daniel Dakota
Course coordinator and examiner: Sara Stymne

News

• 210107: Note that it will be possible to mingle with yur peers in the virtual system gather.town during breaks and after the workshop. You will be able to walk around in a virtual world and talk to groups of peers.
• 210107: The schedule for the final presentations on January 13 are now available. Zoom links are distributed via email and in Studentportalen.
• 200922: Note that the requirements for the proposal has been slightly updated.
• 200902: There was a mistake in the web page schedule. The debate session is not on September 3, but on September 8. September 3 is a standard lecture.
• 200805: First tentative version of the course web page.

Schedule

	Date	Time	Room	Content	Reading
L1	1/9	12-14	Universitetshuset IX and/or Zoom	Introduction, Word embeddings, Sentiment, Cross-lingual
L2	3/9	10-12	Universtietshuset X and Zoom	Science, research, and development	Okasha, Cunningham, Lee
L3	8/9	10-12	Zoom (Note online only!	Science, research, and development 2: debate session	Okasha
S1	14/9	13-15		Seminar - research papers
S2	21/9	10-12		Seminar - research papers (wemb, sent)
S2	22/9	10-12		Seminar - research papers (xling)
L4	23/9	10-12	Blåsenhus 10:K102 and/or Zoom	R&D projects - from proposal to implementation	Zobel 10-11, 13
S3	29/9	13-15		Seminar - research papers
S4	7/10	9-12		Seminar - project proposals
S5	19/10	10-12		Seminar - progress report (sent, wemb)
S5	20/10	10-12		Seminar - progress report (xling)
S6	4/11	10-12		Seminar - progress report, theme: ethics	Hovy and Spruit, 2016
L5	12/11	12-14	Zoom	Dissemination of research results	Zobel 1-9, 14
S7	18/11	10-12		Seminar - progress report
L6	25/11	10-12	Zoom	Review of scientific articles	Zobel 14
S8	3/12	10-12		Seminar - progress report
FS	13/1	8-16	Online	Final workshop - term paper presentations
	13/1	15.45-	gather.town	Social event

All lectures will be given by Sara. The seminars will be led by the seminar leader for each research group. Note that attendance is obligatory at all seminars. Rooms for seminars will be given once it is decided if they are Campus or Zoom based. Decisions for campus and/or Zoom lectures will be announced in good time before each session.

Teaching mode, Covid-related information

Due to the Covid situation the course will be run at least partially in an online format. We hope to be able to switch more to campus activities towards the second half of the term. Lectures will be held either online or in a mixed mode, with a possiblity to attend both online and on campus. Some lectures might get replaced with recorded lectures plus maybe a campus activity. Seminars in smaller groups will be either online or campus-based depending on the preferences of the students in the group. We will try to take your preferences into account when forming the groups.

For all seminars given remotely, we require that students have the camera turned on.

Please respect the current regulations and stay home if you are not feeling well, and maintain social distancing! Note that this also applies to teachers, so any Campus activities may be moved entirely online on short notice. Please always check your email before going to Campus! Note also that while we have booked large classrooms, there is a small risk that a classroom becomes full. In such an unlikely case, we will let students into the classroom on a first come, first served basis, and those arriving when the classroom is full can follow the activity on Zoom instead.

This information will be continually updated throughout the term.

Content

The course gives a theoretical and practical introduction to research and development in language technology. The theoretical part covers basic philosophy of science, research methods in language technology, project planning, and writing and reviewing of scientific papers. The practical part consists of a small project within a research area common to a subgroup of course participants, including a state-of-the-art survey in a reading group, the planning and implementation of a research task, and the writing of a paper according to the standards for scientific publications in language technology. The research areas, with teachers, for 2020 are:

1. Cross-lingual NLP (xling) - Sara Stymne
2. Word embeddings (wemb) - Ali Basirat
3. Sentiment Classification Tasks (sent) - Daniel Dakota

Research Groups

Below groups and articles for the research seminars will appear.

Groups	Members	Papers
Cross-lingual NLP	Bjarki	Sep 14: Yarowsky et al. 2001
	Harm	Sep 14: Tiedemann 2015
	Huiling	Sep 14: Smith et al. 2018
	(Sara)	Sep 21: Artexte et al. 2020
	Yifei	Sep 21: Plank & Agić 2018
	Gustav	Sep 21: Glavaš et al. 2019
	Xingran	Sep 29: Artexte and Schwenk, 2020
	(Sara)	Sep 29: Zoph et al. 2016
	Antonia	Sep 29: Lin et al. 2019
Word embeddings	Ziyang	Sep 14: Mikolov et al. 2013
	Po-Chun	Sep 14: Pennington et al. 2014
	Meichun	Sep 14: Bojanowski et al., 2017
	Ahmed	Sep 21: Luke and Andrew, 2015
	Xi	Sep 21: Nguyen et al., 2017
	(Ali)	Sep 21: Brazinskas et al., 2018
	Maria-Elena	Sep 29: Melamud et al., 2016
	Chuchu	Sep 29: McCann et al., 2017
	(Ali)	Sep 29: Peters et al. 2018
Sentiment classification tasks	(Daniel)	Sep 14: Pang et al., 2002
	Naomi	Sep 14: Kim and Hovy, 2004
	Yongchao	Sep 14: Wiegand et al., 2019
	Sebastian	Sep 21: Pak and Paroubek, 2010
	Sijia	Sep 21: Cortis et al. 2017
	Melvin	Sep 21: Park et al., 2018 2015
	Xindi	Sep 29: Toh and Su 2010
	Giacomo	Sep 29: Attia et al., 2018

Examination

The course is examined by means of five assignments with different weights (see below). In order to pass the course, a student must pass each of one of these. In order to pass the course with distinction, a student must pass at least 50% of the weighted graded assignments with distinction.

Assignments

1. Take home exam on philosophy of science (15%)
   • This assignment will be based on your reading of Okasha's book. You will be asked to discuss issues in the philosophy of science and (sometimes) relate them to the area of language technology. The questions will be handed out September 9, and the report should be handed in September 17.
2. Research paper presentation and discussion (15%)
   • You will present one of the papers discussed in the seminars. The task is to introduce the paper and lead the discussion, not to make a formal presentation (briefly summarize the paper (~2 min), discuss the main points being made, bring up difficult to understand parts, initiate a discussion by proposing themes to discuss). In addition you shall take active part in the discussion of all other papers discussed in the seminars. The seminars have obligatory attendance; if you miss a seminar, you have to write a short report instead. This assignment is not graded and does not qualify for distinction.
3. Project proposal (15%)
   • You will put together a 3-page proposal describing the project you are going to work on for the rest of the course, using an adapted version of the Swedish Research Council's guidelines for research plans. You will also give a short presentation of the proposal in a seminar (8 minutes with slides, plus time for questions and discussions). Your proposal and presentation should be accessible also to non-experts in language technology, so it is important to balance a general description with enough technical details. The deadline for the written proposal is October 2, and the seminars will take place October 7.
4. Review of term papers (15%)
   • You will review two term papers written by your course mates. You will use a set of guidlines which will be specified later. You will receive the papers on December 14 and the reviews are due December 21.
5. Term paper (40%)
   • You will report your project in a paper following the guidelines of Transactions of the Association for Computational Linguistics (except that the page limit for your papers is 4-7 pages + references). The deadline is December 11 for the first version and January 15 for the revised version. On January 13, you will also give an oral presentation of the paper. As part of your work on the project, it is obligatory to attend the progress report seminars. If you miss a seminar, you have to write a short report instead.

Submitting and Reviewing Term papers

Information to appear!

Final Seminar/Workshop

The final seminar will be organized as a workshop with term paper presentations.

Research Groups

Your first task in the course is to make a wish for which research topic to work on. Send a ranked list of your preference for the three topics by email to Sara, at the latest Friday September 4, at 13.00. Please also specify if you prefer to have the semianrs online or on campus (or if you are fine with either option). If you fail to make a wish by this deadline you will be arbitrarily assigned to a topic. We will try our best to respect everyone's wishes, but if it turns out not to be possible, we will resort to random decisions.

Deadlines

Here is a summary of all deadlines in the course.

Task	Deadline	Extra deadline
Choose your preferred topics	September 4, 13:00	-
Hand in take home exam	September 17	November 13
Project proposal	October 2	October 30
Present project proposal	October 7	By agreement
First version of project report	December 11	January 15
Reviews on peer's project papers	December 21	February 19
Final seminar	January 13	By agreement
Final project report	January 15	February 19

Note that it is important for you to finish the course on time, since it is a requirement for starting your master thesis. So try to avoid resorting to the backup deadlines, since it will likely mean you cannot finish the course on time!

Reading

Science and Research

• Okasha, S. (2002) Philosophy of Science: A Very Short Introduction. Oxford University Press. Chapters 1-3 and 5.
• Zobel, J. (2004) Writing for Computer Science. Second Edition. Springer.

• Cunningham, H. (1999) A definition and short history of Language Engineering. Natural Language Engineering 5 (1), 1-16.
• Hovy, D. and Spruit, S. L. (2016) The Social Impact of Natural Language Processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 591-598.
• Lee, L. (2004) "I'm sorry Dave, I'm afraid I can't do that": Linguistics, Statistics, and Natural Language Processing circa 2001. In Computer Science: Reflections on the Field, Reflections from the Field, 111-118.

Cross-Lingual NLP

• Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Eduard Hovy, Kai-Wei Chang, and Nanyun Peng. (2019) On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing. In arXiv preprint, arXiv:1811.00570v3
• Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah Smith. (2016) Many languages, one parser.. In TACL, 4:431-444.
• Mikel Artetxe, Sebastian Ruder, Dani Yogatama, Gorka Labaka, and Eneko Agirre. (2020) A Call for More Rigor in Unsupervised Cross-lingual Learning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics [Sem 2]
• Mikel Artetxe and Holger Schwenk. (2020) Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond. In TACL 7:597-610. [Sem 3]
• Lauriane Aufrant. (2018) Training parsers for low-resourced languages: improving cross-lingual transfer with monolingual knowledge. PhD thesis, Paris Saclay.
• A. Conneau, G. Lample, L. Denoyer, MA. Ranzato, and H. Jégou. (2017) Word Translation Without Parallel Data. arXiv preprint arXiv:1710.04087
• Goran Glavaš, Robert Litschko, Sebastian Ruder, and Ivan Vulić. (2019) How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 710-721.[Sem 2]
• Rob van der Goot, Nikola Ljubešić, Ian Matroos, Malvina Nissim, and Barbara Plank. (2018) Bleaching Text: Abstract Features for Cross-lingual Gender Prediction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 383-389.
• Jiang Guo, Wanxiang Che, Haifeng Wang, and Ting Liu. (2016) A universal framework for inductive transfer parsing across multi-typed treebanks.. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 12-22.
• Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. (2017) Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. In TACL, 5:339-351.
• Miryam de Lhoneux, Johannes Bjerva, Isabelle Augenstein, and Anders Søgaard. (2018) Parameter sharing between dependency parsers for related languages. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 4992-4997.
• Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, and Graham Neubig. (2019) Choosing Transfer Languages for Cross-Lingual Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3125-3135.[Sem 3]
• Stephen Mayhew, Chen-Tse Tsai and Dan Roth. (2017) Cheap Translation for Cross-Lingual Named Entity Recognition. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2536-2545.
• Phoebe Mulcaire, Swabha Swayamdipta, and Noah A. Smith. (2018) Polyglot Semantic Role Labeling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Short Papers), 667-672.
• Tahira Naseem, Regina Barzilay, and Amir Globerson. (2012) Selective sharing for multilingual dependency parsin. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, 629-637.
• Robert Östling and Jörg Tiedemann. (2017) Continuous multilinguality with language vectors. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 644-649.
• Barbara Plank and Željko Agić. (2018) Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 614-620.[Sem 2]
• Edoardo Maria Ponti, Roi Reichart, Anna Korhonen, and Ivan Vulič. (2018) Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1531-1542.
• Aaron Smith, Bernd Bohnet, Miryam de Lhoneux, Joakim Nivre, Yan Shao, and Sara Stymne. (2018) 82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Model. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, 113-123.[Sem 1]
• Jörg Tiedemann. (2012) Character-Based Pivot Translation for Under-Resourced Languages and Domains. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, 141-151.
• Jörg Tiedemann. (2015) Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels. In Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015), 340--349.[Sem 1]
• David Yarowsky, Grace Ngai, and Richard Wicentowski. (2001) Inducing multilingual text analysis tools via robust projection across aligned corpora. In Proceedings of the first international conference on Human language technology research, 1-8.[Sem 1]
• Daniel Zeman, Jan Hajič, Martin Popel, Martin Potthast, Milan Straka, Filip Ginter, Joakim Nivre, and Slav Petrov. (2018) CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, 1-22.
• Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. (2016) Transfer Learning for Low-Resource Neural Machine Translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 1568-1575.[Sem 3]
• Yanyan Zou and Wei Lu. (2018) Learning Cross-lingual Distributed Logical Representations for Semantic Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 673-679.

Word embeddings

• Deerwester, S., Dumais, S.T., Furnas G.W., Landauer, T.K., and Harshman, R. (1990), Indexing by latent semantic analysis, Journal of the American society for information science, 41(6), 391--407.
• Lund, K., and Burgess, C., (1996), Producing high-dimensional semantic spaces from lexical co-occurrence, Behavior research methods, instruments, & computers, 28(2), 203--208.,
• Yoshua, B., Ducharme, R., and Pascal, V., (2001), A Neural Probabilistic Language Model, Advances in Neural Information Processing Systems, 13, 932--938
• Sebastian Padó, Mirella Lapata (2007), Dependency-Based Construction of Semantic Space Models, Computational Linguistics, 33.2
• Sellberg, L., and Jönsson, A., (2008), Using Random Indexing to improve Singular Value Decomposition for Latent Semantic Analysis, Proceedings of the Sixth International Conference on Language Resources and Evaluation
• Mikolov, T., Chen, K., and Corrado, G., and Dean, J., (2013), Efficient Estimation of Word Representations in Vector Space, 1st International Conference on Learning Representations (ICLR), [Sem. 1]
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J., (2013), Distributed Representations of Words and Phrases and their Compositionality, Advances in Neural Information Processing Systems, 26, 3111--3119
• Mikolov, T., Yih, W., and Zweig, G., (2013), Linguistic Regularities in Continuous Space Word Representation, Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, 746--751
• Lebret, R., and Collobert, R., (2014), Word Embeddings through Hellinger PCA, Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
• Pennington, J., and Socher, R., and Manning, C., (2014), GloVe: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), [Sem. 1]
• Neelakantan, A., Shankar, J., Passos, A., McCallum, A., (2014), Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing
• Levy, O.,, and Goldberg, Y., (2014), Dependency-Based Word Embeddings, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics,
• Schnabel, T., Labutov, I., Mimno, D., and Joachims, T., (2015), Evaluation methods for unsupervised word embeddings, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 298--307
• Luke V., and Andrew M., (2015), Word Representations via Gaussian Embedding, International Conference of Learning Representation [Sem. 2]
• Arora, S., Li, Y., Liang, Y., Ma, T., and Risteski, A., (2016), A Latent Variable Model Approach to PMI-based Word Embeddings, Transactions of the Association for Computational Linguistics, 4, 85--399
• Melamud, O., Goldberger, J., and Dagan, I., (2016), Context2vec: Learning Generic Context Embedding with Bidirectional LSTM, Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, 51--61, [Sem. 3]
• Bojanowski, P., and Grave, E., and Joulin, A., and Mikolov, T., (2017), Enriching Word Vectors with Subword Information, Transactions of the Association for Computational Linguistics, 5, 135--146, [Sem. 1]
• Peters, M., Ammar, W., Bhagavatula, C., and Power, R., (2017), Semi-supervised sequence tagging with bidirectional language models, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 1, 1756--1765
• Nguyen, D.Q., Nguyen, D.Q., Modi, A., Thater, S., and Pinkal, M., (2017), A Mixture Model for Learning Multi-Sense Word Embeddings, Proceedings of the 6th Joint Conference on Lexical and Computational Semantics, 121--127, [Sem. 2]
• McCann, B. Bradbury, J. Xiong, C. and Socher, R (2017), Learned in Translation: Contextualized Word Vectors, Advances in Neural Information Processing Systems 30 (NIPS 2017), [Sem. 3]
• Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L., (2018), Deep Contextualized Word Representations, Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 2227--2237, [Sem. 3]
• Brazinskas, A., Havrylov, S., Titov, I., (2018), Embedding Words as Distributions with a Bayesian Skip-gram Model, Proceedings of the 27th International Conference on Computational Linguistics, 1775--1789, [Sem. 2]
• Devlin, J., Chang, M., Lee, K., and Toutanova, K., (2019), BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171--4186,
• more information

Sentiment Classification Tasks

• Bo Pang, Lillian Lee, Shivakumar Vaithyanathan, (2002), Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), 79--86 [Sem 1]
• Soo-Min Kim and Eduard Hovy, (2004), Determining the sentiment of opinions, COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics, 1367--1373 [Sem 1]
• Alexander Pak and Patrick Paroubek, (2010), Twitter as a Corpus for Sentiment Analysis and Opinion Mining, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10) [Sem 2]
• Apoorv Agarwal, Boyi Xie, Ilia Vovsha, Owen Rambow, Rebecca Passonneau, (2011), Sentiment analysis of twitter data, Proceedings of Workshop on Language in Social Media (LSM 2011), 30--38
• Andrew Maas, Raymond Daly, Peter Pham, Dan Huang, Andrew Ng, Chrisopher Potts, (2011), Learning Word Vectors for Sentiment Analysis, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 142--150
• Cicero Nogueira dos Santos, Maira Gatti, (2014), Deep Convolution Neural Networks for Sentiment Analysis of Short Texts, Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 69--78
• Yequan Wang, Minlie Huang, Li Zhao, Xiaoyan Zhu, (2016), Attention-based LSTM for Aspect-level Sentiment Classification, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 606--615
• Maria Pontiki, Dimitrios Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphee de Clercq, Veronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeny Kotelnikov, Nuria Bel, Salud María Jimenez-Zafra, Gülsen Eryigit, (2016), SemEval-2016 Task 5: Aspect Based Sentiment Analysis, Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 19--30
• Zhiqiang Toh, Jian Su (2016), NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features, Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 282--288 [Sem 3]
• Peng Chen, Zhongqian Sun, Lidong Bing, Wei Yang, (2017), Recurrent Attention Network on Memory for Aspect Sentiment Analysis, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 452--461
• Sara Rosenthal, Noura Farra, Preslav Nakov, (2017), SemEval-2017 Task 4: Sentiment Analysis in Twitter, Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 502--518
• Christo Baziotis, Nikos Pelekis, Christos Doulkeridis, (2017), DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis, Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 747--754
• Keith Cortis, André Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh, Brian Davis (2017), SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News, Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 519--535
• Youness Mansar and Lorenzo Gatti and Sira Ferradans and Marco Guerini and Jacop Staiano, (2017), Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines, Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 817--822 [Sem 2]
• Anna Schmidt, Michael Wiegand (2017), A Survey of Hate Speech Detection Using Natural Language Processing, Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, 1--10
• Mohammed Attia, Younes Samih, Ali Elkahky, Laura Kallmeyer (2018), Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 635--640 [Sem 3]
• Ji Ho Park, Jamin Shin, Pascale Fung (2018), Reducing Gender Bias in Abusive Language Detection, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2799--2804 [Sem 2]
• Michael Wiegand, Josef Ruppenhofer, Thomas Kleinbauer (2019), Detection of Abusive Language: the Problem of Biased Datasets, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 602--608 [Sem 1]