| 1. |
- Barddal, Johanna, et al.
(författare)
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Reconstructing constructional semantics : The dative subject construction in Old Norse-Icelandic, Latin, Ancient Greek, Old Russian and Old Lithuanian
- 2012
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Ingår i: Studies in Language. - : John Benjamins Publishing Company. - 0378-4177 .- 1569-9978. ; 36:3, s. 511-547
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Tidskriftsartikel (refereegranskat)abstract
- As the historical linguistic community is well aware, reconstructing semantics is a notoriously difficult undertaking. Such reconstruction has so far mostly been carried out on lexical items, like words and morphemes, and has not been conducted for larger and more complex linguistic units, which intuitively seems to be a more intricate task, especially given the lack of methodological criteria and guidelines within the field. This follows directly from the fact that most current theoretical frameworks are not construction-based, that is, they do not assume that constructions are form-meaning correspondences. In order to meet this challenge, we present an attempt at reconstructing constructional semantics, and more precisely the semantics of the Dative Subject Construction for an earlier stage of Indo-European. For this purpose we employ lexical semantic verb classes in combination with the semantic map model (Bar partial derivative dal 2007, Bar partial derivative dal, Kristoffersen & Sveen 2011), showing how incredibly stable semantic fields may remain across long time spans, and how reconstructing such semantic fields may be accomplished
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| 2. |
- McGillivray, Barbara, et al.
(författare)
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A new corpus annotation framework for Latin diachronic lexical semantics
- 2022
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Ingår i: Journal of Latin Linguistics. - : Walter de Gruyter. - 2194-8739 .- 2194-8747. ; 21:1, s. 47-105
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Tidskriftsartikel (refereegranskat)abstract
- We present a new corpus-based resource and methodology for the annotation of Latin lexical semantics, consisting of 2,399 annotated passages of 40 lemmas from the Latin diachronic corpus LatinISE. We also describe how the annotation was designed, analyse annotators' styles, and present the preliminary results of a study on the lexical semantics and diachronic change of the 40 lemmas. We complement this analysis with a case study on semantic vagueness. As the availability of digital corpora of ancient languages increases, and as computational research develops new methods for large-scale analysis of diachronic lexical semantics, building lexical semantic annotation resources can shed new light on large-scale patterns in the semantic development of lexical items over time. We share recommendations for designing the annotation task that will hopefully help similar research on other less-resourced or historical languages.
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| 3. |
- McGillivray, Barbara, et al.
(författare)
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The challenges and prospects of the intersection of humanities and data science: A White Paper from The Alan Turing Institute
- 2020
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Ingår i: White paper.
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Since their beginnings, the digital humanities have engaged in an energetic debate about their scope, defining features, and relationship to the wider humanities, and have established themselves as a community of practice (Schreibman et al., 2004; Terras, 2010; Terras, 2013; Terras et al., 2013; Gold and Klein, 2016; The Digital Humanities Manifesto 2.0). The computational focus has characterised the field from its initial explorations (Hockey, 2004; Vanhoutte, 2013; Nyhan and Flinn, 2016) and the shift from the label ‘Humanities Computing’ to ‘Digital Humanities’ was a catalyst for change. In the history of the field, recurring cycles and productive tensions have arisen from the interfolding of computational methodologies and approaches with hermeneutic and critical modes of analysis (see McCarty, 2005; Rockwell and Sinclair, 2016; Jones, 2016). This document postulates that we are currently witnessing another one of these junctures, one that is calling for a critical involvement with data science. In many ways, we are seeing earlier methods blending into, or being extended by data science. Digitisation workflows are being augmented with automatic information extraction, data analysis, automated transcription of handwritten documents, and visualisation of transcribed content. Techniques developed for history, literary studies, and linguistics are being scaled towards larger datasets and more complex problems raising the bar of interpretability and questioning the validity of data collection and analysis methods. On the other hand, the field of data science has recently started to engage with non-STEM (Science, Technology, Engineering, and Mathematics) disciplines, by offering new data-driven modelling frameworks for addressing long-standing research questions (Kitchin, 2014; Lazer et al., 2009) and proposing so-called ‘human-centred approaches’ to data science, focussed on the interpretability of machine learning models and a more active role for human input in algorithms (See Chen et al., 2016). Moreover, in the current historical context we are witnessing an increased awareness of the questions of diversity and inclusion in research and academia, and we are seeing the creation of a strong movement aimed at addressing such issues globally. We believe that this paper can play a role in reinforcing a positive message in this respect.
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| 4. |
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| 5. |
- Perrone, Valerio, et al.
(författare)
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Lexical semantic change for Ancient Greek and Latin
- 2021
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Ingår i: Computational approaches to semantic change. - Berlin : Language Science Press. - 9783985540082 ; , s. 287-310
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Bokkapitel (refereegranskat)abstract
- Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associating a word with its correct meaning in its historical context is a central challenge in diachronic research. Historical corpora of classical languages, such as Ancient Greek and Latin, typically come with rich metadata, and existing models are limited by their inability to exploit contextual information beyond the document timestamp. While embedding-based methods feature among the current state of the art systems, they are lacking in their interpretative power. In contrast, Bayesian models provide explicit and interpretable representations of semantic change phenomena. In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model. In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on text genres. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-ofthe-art embedding-based models. On top of providing a full description of meaning change over time, we show that Bayesian mixture models are highly competitive approaches to detect binary semantic change in both Ancient Greek and Latin.
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| 6. |
- Schlechtweg, Dominik, et al.
(författare)
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Post-Evaluation Data for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
- 2020
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Ingår i: Zenodo. - : Zenodo.
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This data collection contains the post-evaluation data for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection: (1) the starting kit to download data, and examples for competing in the CodaLab challenge including baselines; (2) the true binary change scores of the targets for Subtask 1, and their true graded change scores for Subtask 2 (test_data_truth/); (3)the scoring program used to score submissions against the true test data in the evaluation and post-evaluation phase (scoring_program/); and (4) the results of the evaluation phase including, for example, analysis plots (plots/) displaying the results:
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| 7. |
- Schlechtweg, Dominik, et al.
(författare)
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SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
- 2020
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Ingår i: Proceedings of the Fourteenth Workshop on Semantic Evaluation (SemEval2020), Barcelona, Spain (Online), December 12, 2020.. - : ACL.
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Konferensbidrag (refereegranskat)abstract
- Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
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| 8. |
- Tahmasebi, Nina, 1982, et al.
(författare)
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Swedish Test Data for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection
- 2020
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Ingår i: Zenodo.
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This data collection contains the Swedish test data for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. It consists of a Swedish text corpus pair (corpus1/, corpus2/) and 31 lemmas which have been annotated for their lexical semantic change between the two corpora (targets.txt). We sample from the KubHist2 corpus, digitized by the National Library of Sweden, and available through the Språkbanken corpus infrastructure Korp (Borin et al., 2012). The full corpus is available through a CC BY (attribution) license. Each word for which the lemmatizer in the Korp pipeline has found a lemma is replaced with the lemma. In cases where the lemmatizer cannot find a lemma, we leave the word as is (i.e., unlemmatized, no lower-casing). KubHist contains very frequent OCR errors, especially for the older data.More detail about the properties and quality of the Kubhist corpus can be found in (Adesam et al., 2019).
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