Incorporating Emoji Descriptions Improves Tweet Classification

Article presenting a simple strategy to process emojis in Tweets: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. Results show that this strategy is more effective than using pretrained emoji embeddings for tweet classification

Limsiiles: Basic english substitution for student answer assessment at semeval 2013

International audienceIn this paper, we describe a method for assessing student answers, modeled as a paraphrase identification problem, based on substitution by Basic English variants. Basic English paraphrases are acquired from the Simple English Wiktionary. Substitutions are applied both on reference answers and student answers in order to reduce the diversity of their vocabulary and map them to a common vocabulary. The evaluation of our approach on the SemEval 2013 Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge data shows promising results, and this work is a first step toward an open-domain system able to exhibit deep text understanding capabilities

Discovering multiword expressions

In this paper, we provide an overview of research on multiword expressions (MWEs), from a natural lan- guage processing perspective. We examine methods developed for modelling MWEs that capture some of their linguistic properties, discussing their use for MWE discovery and for idiomaticity detection. We con- centrate on their collocational and contextual preferences, along with their fixedness in terms of canonical forms and their lack of word-for-word translatatibility. We also discuss a sample of the MWE resources that have been used in intrinsic evaluation setups for these methods

SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)

Proceedings of: International Workshop on Semantic Evaluation. SemEval-2013 : Semantic Evaluation Exercises. Took place in 2013 June, 14-15, in Atlanta, Georgia (USA). The event Web site in http://www.cs.york.ac.uk/semeval-2013/The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug interactions that appear in biomedical literature. We propose two subtasks for the DDIExtraction 2013 Shared Task challenge: 1) the recognition and classification of drug names and 2) the extraction and classification of their interactions. Both subtasks have been very successful in participation and results. There were 14 teams who submitted a total of 38 runs. The best result reported for the first subtask was F1 of 71.5% and 65.1% for the second one.This research work has been supported by the Regional Government of Madrid under the Research Network MA2VICMR (S2009/TIC-1542), by the Spanish Ministry of Education under the project MULTIMEDICA (TIN2010-20644-C03-01).Publicad

Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model

Sentence Representation Learning (SRL) is a fundamental task in Natural Language Processing (NLP), with Contrastive learning of Sentence Embeddings (CSE) as the mainstream technique due to its superior performance. An intriguing phenomenon in CSE is the significant performance gap between supervised and unsupervised methods, even when their sentence encoder and loss function are the same. Previous works attribute this performance gap to differences in two representation properties (alignment and uniformity). However, alignment and uniformity only measure the results, which means they cannot answer "What happens during the training process that leads to the performance gap?" and "How can the performance gap be narrowed?". In this paper, we conduct empirical experiments to answer these "What" and "How" questions. We first answer the "What" question by thoroughly comparing the behavior of supervised and unsupervised CSE during their respective training processes. From the comparison, We observe a significant difference in fitting difficulty. Thus, we introduce a metric, called Fitting Difficulty Increment (FDI), to measure the fitting difficulty gap between the evaluation dataset and the held-out training dataset, and use the metric to answer the "What" question. Then, based on the insights gained from the "What" question, we tackle the "How" question by increasing the fitting difficulty of the training dataset. We achieve this by leveraging the In-Context Learning (ICL) capability of the Large Language Model (LLM) to generate data that simulates complex patterns. By utilizing the hierarchical patterns in the LLM-generated data, we effectively narrow the gap between supervised and unsupervised CSE.Comment: work in progres

CoMiC: Exploring Text Segmentation and Similarity in the English Entrance Exams Task. CLEF

Abstract. This paper describes our contribution to the English Entrance Exams task of CLEF 2015, which requires participating systems to automatically solve multiple choice reading comprehension tasks. We use a combination of text segmentation and different similarity measures with the aim of exploiting two observed aspects of tests: 1) the often linear relationship between reading text and test questions and 2) the differences in linguistic encoding of content in distractor answers vs. the correct answer. Using features based on these characteristics, we train a ranking SVM in order to learn answer preferences. In the official 2015 competition we achieve a c@1 score of 0.29, a medium but encouraging result. We identify two main issues that pave the way towards further research

Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter

[EN] Human communication using natural language, specially in social media, is influenced by the use of figurative language like irony. Recently, several workshops are intended to explore the task of irony detection in Twitter by using computational approaches. This paper describes a model for irony detection based on the contextualization of pre-trained Twitter word embeddings by means of the Transformer architecture. This approach is based on the same powerful architecture as BERT but, differently to it, our approach allows us to use in-domain embeddings. We performed an extensive evaluation on two corpora, one for the English language and another for the Spanish language. Our system was the first ranked system in the Spanish corpus and, to our knowledge, it has achieved the second-best result on the English corpus. These results support the correctness and adequacy of our proposal. We also studied and interpreted how the multi-head self-attention mechanisms are specialized on detecting irony by means of considering the polarity and relevance of individual words and even the relationships among words. This analysis is a first step towards understanding how the multi-head self-attention mechanisms of the Transformer architecture address the irony detection problem.This work has been partially supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Pla Santamaría, F. (2020). Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter. Information Processing & Management. 57(4):1-15. https://doi.org/10.1016/j.ipm.2020.102262S115574Farías, D. I. H., Patti, V., & Rosso, P. (2016). Irony Detection in Twitter. ACM Transactions on Internet Technology, 16(3), 1-24. doi:10.1145/2930663Greene, R., Cushman, S., Cavanagh, C., Ramazani, J., & Rouzer, P. (Eds.). (2012). The Princeton Encyclopedia of Poetry and Poetics. doi:10.1515/9781400841424Van Hee, C., Lefever, E., & Hoste, V. (2018). We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter. Computational Linguistics, 44(4), 793-832. doi:10.1162/coli_a_00337Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic Sarcasm Detection. ACM Computing Surveys, 50(5), 1-22. doi:10.1145/3124420Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019). Albert: A lite bert for self-supervised learning of language representations.Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.xMuecke, D. C. (1978). Irony markers. Poetics, 7(4), 363-375. doi:10.1016/0304-422x(78)90011-6Potamias, R. A., Siolas, G., & Stafylopatis, A. (2019). A transformer-based approach to irony and sarcasm detection. arXiv:1911.10401.Rosso, P., Rangel, F., Farías, I. H., Cagnina, L., Zaghouani, W., & Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass, 12(4), e12275. doi:10.1111/lnc3.12275Sulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035Wilson, D., & Sperber, D. (1992). On verbal irony. Lingua, 87(1-2), 53-76. doi:10.1016/0024-3841(92)90025-eYus, F. (2016). Propositional attitude, affective attitude and irony comprehension. Pragmatics & Cognition, 23(1), 92-116. doi:10.1075/pc.23.1.05yusZhang, S., Zhang, X., Chan, J., & Rosso, P. (2019). Irony detection via sentiment-based transfer learning. Information Processing & Management, 56(5), 1633-1644. doi:10.1016/j.ipm.2019.04.00