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| 2. |
- Haffenden, Chris, 1982-, et al.
(författare)
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BERTopic for Swedish: Topic modeling made easier via KB-BERT
- 2022
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Annan publikation (populärvet., debatt m.m.)abstract
- Topic modeling is an exciting option for exploring and finding patterns in large volumes of text data. While this previously required considerable programming skills, a recent innovation has simplified the method to make it more accessible for researchers in and beyond the academy. We explain how BERTopic harnesses KBLab’s language models to produce state-of-the-art topic modeling, and we offer some tips on how to get started.
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| 3. |
- Haffenden, Chris, 1982-, et al.
(författare)
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Digital humaniora eller humanistisk datavetenskap?
- 2022
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Annan publikation (populärvet., debatt m.m.)abstract
- På KB-labb används artificiell intelligens (AI) för att möjliggöra ny forskning på KB:s samlingar. När AI-verktyg blir alltmer etablerade väcks frågan om vad människor, respektive maskiner, ska göra i forskningsprojekt framöver.
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| 4. |
- Haffenden, Chris, 1982-, et al.
(författare)
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Making and Using AI in the Library : Creating a BERT Model at the National Library of Sweden
- 2023
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Ingår i: College & Research Libraries. - : American Library Association. - 0010-0870 .- 2150-6701. ; 84:1
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- How can novel AI techniques be made and put to use in the library? Combining methods from data and library science, this article focuses on Natural Language Processing technologies in especially national libraries. It explains how the National Library of Sweden’s collections enabled the development of a new BERT language model for Swedish. It also outlines specific use cases for the model in the context of academic libraries, detailing strategies for how such a model could make digital collections available for new forms of research: from automated classification to enhanced searchability and improved OCR cohesion. Highlighting the potential for cross-fertilizing AI with libraries, the conclusion suggests that while AI may transform the workings of the library, libraries can also have a key role to play in the future development of AI.
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| 5. |
- Kulmizev, Artur, et al.
(författare)
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Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing – A Tale of Two Parsers Revisited
- 2019
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Ingår i: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). ; , s. 2755-2768
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Konferensbidrag (refereegranskat)abstract
- Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural features but suffer from error propagation, while graph-based parsers benefit from global optimization but have restricted feature scope. In this paper, we show that, even though some details of the picture have changed after the switch to neural networks and continuous representations, the basic trade-off between rich features and global optimization remains essentially the same. Moreover, we show that deep contextualized word embeddings, which allow parsers to pack information about global sentence structure into local feature representations, benefit transition-based parsers more than graph-based parsers, making the two approaches virtually equivalent in terms of both accuracy and error profile. We argue that the reason is that these representations help prevent search errors and thereby allow transitionbased parsers to better exploit their inherent strength of making accurate local decisions. We support this explanation by an error analysis of parsing experiments on 13 languages.
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