The Experts below are selected from a list of 16212 Experts worldwide ranked by ideXlab platform
Ekaterina Shutova - One of the best experts on this subject based on the ideXlab platform.
-
a report on the 2018 vua metaphor detection shared task
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Chee Wee Leong, Beata Beigman Klebanov, Ekaterina ShutovaAbstract:As the community working on computational approaches to Figurative Language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.
-
semeval 2015 task 11 sentiment analysis of Figurative Language in twitter
North American Chapter of the Association for Computational Linguistics, 2015Co-Authors: Aniruddha Ghosh, Paolo Rosso, Tony Veale, Ekaterina Shutova, John A Barnden, Antonio ReyesAbstract:This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of Figurative Language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing Figurative Language on Twitter. Specifically, three broad classes of Figurative Language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.
-
a computational model of logical metonymy
ACM Transactions on Speech and Language Processing, 2013Co-Authors: Ekaterina Shutova, Jakub Kaplan, Simone Teufel, Anna KorhonenAbstract:The use of Figurative Language is ubiquitous in natural Language texts and it is a serious bottleneck in automatic text understanding. A system capable of interpreting Figurative expressions would be an invaluable addition to the real-world natural Language processing (NLP) applications that need to access semantics, such as machine translation, opinion mining, question answering and many others. In this article we focus on one type of Figurative Language, logical metonymy, and present a computational model of its interpretation bringing together statistical techniques and the insights from linguistic theory. Compared to previous approaches this model is both more informative and more accurate. The system produces sense-level interpretations of metonymic phrases and then automatically organizes them into conceptual classes, or roles, discussed in the majority of linguistic literature on the phenomenon.
Kari-anne B. Næss - One of the best experts on this subject based on the ideXlab platform.
-
Figurative Language comprehension in individuals with autism spectrum disorder: A meta-analytic review:
Autism, 2016Co-Authors: Tamar Kalandadze, Courtenay Frazier Norbury, Terje Nærland, Kari-anne B. NæssAbstract:We present a meta-analysis of studies that compare Figurative Language comprehension in individuals with autism spectrum disorder and in typically developing controls who were matched based on chronological age or/and Language ability. A total of 41 studies and 45 independent effect sizes were included based on predetermined inclusion criteria. Group matching strategy, age, types of Figurative Language, and cross-linguistic differences were examined as predictors that might explain heterogeneity in effect sizes. Overall, individuals with autism spectrum disorder showed poorer comprehension of Figurative Language than their typically developing peers (Hedges’ g = –0.57). A meta-regression analysis showed that group matching strategy and types of Figurative Language were significantly related to differences in effect sizes, whereas chronological age and cross-linguistic differences were not. Differences between the autism spectrum disorder and typically developing groups were small and nonsignificant when t...
Paolo Rosso - One of the best experts on this subject based on the ideXlab platform.
-
sentiment polarity classification of Figurative Language exploring the role of irony aware and multifaceted affect features
International Conference on Computational Linguistics, 2017Co-Authors: Delia Irazu Hernandez Farias, Cristina Bosco, Viviana Patti, Paolo RossoAbstract:The presence of Figurative Language represents a big challenge for sentiment analysis. In this work, we address the task of assigning sentiment polarity to Twitter texts when Figurative Language is employed, with a special focus on the presence of ironic devices. We introduce a pipeline model which aims to assign a polarity value exploiting, on the one hand, irony-aware features, which rely on the outcome of a state-of-the-art irony detection model, on the other hand a wide range of affective features that cover different facets of affect exploiting information from various sentiment and emotion lexical resources for English available to the community, possibly referring to different psychological models of affect. The proposed method has been evaluated on a set of tweets especially rich in Figurative Language devices proposed as a benchmark in the shared task on “Sentiment Analysis of Figurative Language” at SemEval-2015. Experiments and results of feature ablation show the usefulness of irony-aware features and the impact of using different affective lexicons for the task.
-
semeval 2015 task 11 sentiment analysis of Figurative Language in twitter
North American Chapter of the Association for Computational Linguistics, 2015Co-Authors: Aniruddha Ghosh, Paolo Rosso, Tony Veale, Ekaterina Shutova, John A Barnden, Antonio ReyesAbstract:This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of Figurative Language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing Figurative Language on Twitter. Specifically, three broad classes of Figurative Language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.
-
from humor recognition to irony detection the Figurative Language of social media
Data and Knowledge Engineering, 2012Co-Authors: Antonio Reyes, Paolo Rosso, Davide BuscaldiAbstract:The research described in this paper is focused on analyzing two playful domains of Language: humor and irony, in order to identify key values components for their automatic processing. In particular, we are focused on describing a model for recognizing these phenomena in social media, such as ''tweets''. Our experiments are centered on five data sets retrieved from Twitter taking advantage of user-generated tags, such as ''#humor'' and ''#irony''. The model, which is based on textual features, is assessed on two dimensions: representativeness and relevance. The results, apart from providing some valuable insights into the creative and Figurative usages of Language, are positive regarding humor, and encouraging regarding irony.
Petra B Schumacher - One of the best experts on this subject based on the ideXlab platform.
-
the role of literal meaning in Figurative Language comprehension evidence from masked priming erp
Frontiers in Human Neuroscience, 2014Co-Authors: Hanna Weiland, Valentina Bambini, Petra B SchumacherAbstract:The role of literal meaning during the construction of meaning that goes beyond pure literal composition was investigated by combining cross-modal masked priming and ERPs. This experimental design was chosen to compare two conflicting theoretical positions on this topic. The indirect access account claims that literal aspects are processed first, and additional meaning components are computed only if no satisfactory interpretation is reached. In contrast, the direct access approach argues that Figurative aspects can be accessed immediately. We presented metaphors (These lawyers are hyenas, Experiment 1a & 1b) and producer-for-product metonymies (The boy read Boll, Experiment 2a & 2b) with and without a prime word that was semantically relevant to the literal meaning of the target word (furry and talented, respectively). In the presentation without priming, metaphors revealed a biphasic N400-Late Positivity pattern, while metonymies showed an N400 only. We interpret the findings within a two-phase Language architecture where contextual expectations guide initial access (N400) and precede pragmatic adjustment resulting in reconceptualization (Late Positivity). With masked priming, the N400-difference was reduced for metaphors and vanished for metonymies. This speaks against the direct access view that predicts a facilitating effect for the literal condition only and hence would predict the N400-difference to increase. The results are more consistent with indirect access accounts that argue for facilitation effects for both conditions and consequently for consistent or even smaller N400-amplitude differences. This combined masked priming ERP paradigm therefore yields new insights into the role of literal meaning in the online composition of Figurative Language.
Chee Wee Leong - One of the best experts on this subject based on the ideXlab platform.
-
a report on the 2020 vua and toefl metaphor detection shared task
Meeting of the Association for Computational Linguistics, 2020Co-Authors: Chee Wee Leong, Beata Beigman Klebanov, Chris Hamill, Egon Stemle, Rutuja Ubale, Xianyang ChenAbstract:In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language.
-
a report on the 2018 vua metaphor detection shared task
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Chee Wee Leong, Beata Beigman Klebanov, Ekaterina ShutovaAbstract:As the community working on computational approaches to Figurative Language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.