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- Andersson, Marta, et al.
(författare)
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A sentiment-annotated dataset of English causal connectives
- 2020
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Ingår i: Proceedings of the 14th Linguistic Annotation Workshop. - 9781952148330 ; , s. 24-33
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the semantic prosody of three causal connectives: due to, owing to and because of in seven varieties of the English language. While research in the domain of English causality exists, we are not aware of studies that would cover the domain of causal connectives in English. Our claim is that connectives such as because of link two arguments, (at least) one of which will include a phrase that contributes to the interpretation of the relation as positive or negative, and hence define the prosody of the connective used. As our results demonstrate, the majority of the prosodies identified are negative for all three connectives; the proportions are stable across the varieties of English studied, and contrary to our expectations, we find no significant differences between the functions of the connectives and discourse preferences. Further, we investigate whether automatizing the sentiment annotation procedure via a simple language-model based classifier is possible. The initial results highlights the complexity of the task and the need for complicated systems, probably aided with other related datasets to achieve reasonable performance.
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- Buchanan, E. M., et al.
(författare)
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The Psychological Science Accelerator's COVID-19 rapid-response dataset
- 2023
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.
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- Kurfali, Murathan, et al.
(författare)
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A distantly supervised Grammatical Error Detection/Correction system for Swedish
- 2023
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Ingår i: Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning. - 9789180752503 ; , s. 35-39
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Konferensbidrag (refereegranskat)abstract
- This paper presents our submission to the first Shared Task on Multilingual Grammatical Error Detection (MultiGED-2023). Our method utilizes a transformer-based sequence-to-sequence model, which was trained on a synthetic dataset consisting of 3.2 billion words. We adopt a distantly supervised approach, with the training process relying exclusively on the distribution of language learners' errors extracted from the annotated corpus used to construct the training data. In the Swedish track, our model ranks fourth out of seven submissions in terms of the target F0.5 metric, while achieving the highest precision. These results suggest that our model is conservative yet remarkably precise in its predictions.
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- Kurfali, Murathan, 1990-, et al.
(författare)
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A Multi-Word Expression Dataset for Swedish
- 2020
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Ingår i: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). - Marseille : European Language Resources Association (ELRA). ; , s. 4402-4409
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Konferensbidrag (refereegranskat)abstract
- We present a new set of 96 Swedish multi-word expressions annotated with degree of (non-)compositionality. In contrast to most previous compositionality datasets we also consider syntactically complex constructions and publish a formal specification of each expression. This allows evaluation of computational models beyond word bigrams, which have so far been the norm. Finally, we use the annotations to evaluate a system for automatic compositionality estimation based on distributional semantics. Our analysis of the disagreements between human annotators and the distributional model reveal interesting questions related to the perception of compositionality, and should be informative to future work in the area.
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| 7. |
- Kurfalı, Murathan, 1990-, et al.
(författare)
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Breaking the Narrative: Scene Segmentation through Sequential Sentence Classification
- 2021
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe our submission to the Shared Task on Scene Segmentation (STSS). The shared task requires participants to segment novels into coherent segments, called scenes. We approach this as a sequential sentence classification task and offer a BERT-based solution with a weighted cross-entropy loss. According to the results, the proposed approach performs relatively well on the task as our model ranks first and second, in official in-domain and out-domain evaluations, respectively. However, the overall low performances (0.37 F1-score) suggest that there is still much room for improvement.
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| 8. |
- Kurfalı, Murathan, 1990-
(författare)
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Contributions to Shallow Discourse Parsing : To English and beyond
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Discourse is a coherent set of sentences where the sequential reading of the sentences yields a sense of accumulation and readers can easily follow why one sentence follows another. A text that lacks coherence will most certainly fail to communicate its intended message and leave the reader puzzled as to why the sentences are presented together. However, formally accounting for the differences between a coherent and a non-coherent text still remains a challenge. Various theories propose that the semantic links that are inferred between sentences/clauses, known as discourse relations, are the building blocks of the discourse that can be connected to one another in various ways to form the discourse structure. This dissertation focuses on the former problem of discovering such discourse relations without aiming to arrive at any structure, a task known as shallow discourse parsing (SDP). Unfortunately, so far, SDP has been almost exclusively performed on the available gold annotations in English, leading to only limited insight into how the existing models would perform in a low-resource scenario potentially involving any non-English language. The main objective of the current dissertation is to address these shortcomings and help extend SDP to the non-English territory. This aim is pursued through three different threads: (i) investigation of what kind of supervision is minimally required to perform SDP, (ii) construction of multilingual resources annotated at discourse-level, (iii) extension of well-known means to (SDP-wise) low-resource languages. An additional aim is to explore the feasibility of SDP as a probing task to evaluate discourse-level understanding abilities of modern language models is also explored.The dissertation is based on six papers grouped in three themes. The first two papers perform different subtasks of SDP through relatively understudied means. Paper I presents a simplified method to perform explicit discourse relation labeling without any feature-engineering whereas Paper II shows how implicit discourse relation recognition benefits from large amounts of unlabeled text through a novel method for distant supervision. The third and fourth papers describe two novel multilingual discourse resources, TED-MDB (Paper III) and three bilingual discourse connective lexicons (Paper IV). Notably, Ted-MDB is the first parallel corpus annotated for PDTB-style discourse relations covering six non-English languages. Finally, the last two studies directly deal with multilingual discourse parsing where Paper V reports the first results in cross-lingual implicit discourse relation recognition and Paper VI proposes a multilingual benchmark including certain discourse-level tasks that have not been explored in this context before. Overall, the dissertation allows for a more detailed understanding of what is required to extend shallow discourse parsing beyond English. The conventional aspects of traditional supervised approaches are replaced in favor of less knowledge-intensive alternatives which, nevertheless, achieve state-of-the-art performance in their respective settings. Moreover, thanks to the introduction of TED-MDB, cross-lingual SDP is explored in a zero-shot setting for the first time. In sum, the proposed methodologies and the constructed resources are among the earliest steps towards building high-performance multilingual, or non-English monolingual, shallow discourse parsers.
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