-
Morris, V.: Automated language identification of bibliographic resources (2020)
0.08
0.07525398 = product of:
0.11288096 = sum of:
0.08516933 = weight(_text_:resources in 5749) [ClassicSimilarity], result of:
0.08516933 = score(doc=5749,freq=4.0), product of:
0.18665522 = queryWeight, product of:
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.051133685 = queryNorm
0.45629224 = fieldWeight in 5749, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.0625 = fieldNorm(doc=5749)
0.027711634 = product of:
0.055423267 = sum of:
0.055423267 = weight(_text_:22 in 5749) [ClassicSimilarity], result of:
0.055423267 = score(doc=5749,freq=2.0), product of:
0.17906146 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051133685 = queryNorm
0.30952093 = fieldWeight in 5749, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=5749)
0.5 = coord(1/2)
0.6666667 = coord(2/3)
- Abstract
- This article describes experiments in the use of machine learning techniques at the British Library to assign language codes to catalog records, in order to provide information about the language of content of the resources described. In the first phase of the project, language codes were assigned to 1.15 million records with 99.7% confidence. The automated language identification tools developed will be used to contribute to future enhancement of over 4 million legacy records.
- Date
- 2. 3.2020 19:04:22
-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
0.03
0.027071282 = product of:
0.08121385 = sum of:
0.08121385 = product of:
0.24364153 = sum of:
0.24364153 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
0.24364153 = score(doc=862,freq=2.0), product of:
0.43351194 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.051133685 = queryNorm
0.56201804 = fieldWeight in 862, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
8.478011 = idf(docFreq=24, maxDocs=44218)
0.046875 = fieldNorm(doc=862)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Source
- https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
-
Suissa, O.; Elmalech, A.; Zhitomirsky-Geffet, M.: Text analysis using deep neural networks in digital humanities and information science (2022)
0.02
0.017743612 = product of:
0.053230833 = sum of:
0.053230833 = weight(_text_:resources in 491) [ClassicSimilarity], result of:
0.053230833 = score(doc=491,freq=4.0), product of:
0.18665522 = queryWeight, product of:
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.051133685 = queryNorm
0.28518265 = fieldWeight in 491, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.0390625 = fieldNorm(doc=491)
0.33333334 = coord(1/3)
- Abstract
- Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super-human performance. DNNs are the state-of-the-art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of "right" and "wrong" examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use-cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community.
-
Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022)
0.02
0.017565278 = product of:
0.052695833 = sum of:
0.052695833 = weight(_text_:resources in 1139) [ClassicSimilarity], result of:
0.052695833 = score(doc=1139,freq=2.0), product of:
0.18665522 = queryWeight, product of:
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.051133685 = queryNorm
0.28231642 = fieldWeight in 1139, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.650338 = idf(docFreq=3122, maxDocs=44218)
0.0546875 = fieldNorm(doc=1139)
0.33333334 = coord(1/3)
- Abstract
- In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
-
¬Der Student aus dem Computer (2023)
0.02
0.01616512 = product of:
0.04849536 = sum of:
0.04849536 = product of:
0.09699072 = sum of:
0.09699072 = weight(_text_:22 in 1079) [ClassicSimilarity], result of:
0.09699072 = score(doc=1079,freq=2.0), product of:
0.17906146 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051133685 = queryNorm
0.5416616 = fieldWeight in 1079, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=1079)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 27. 1.2023 16:22:55
-
Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023)
0.01
0.009237211 = product of:
0.027711634 = sum of:
0.027711634 = product of:
0.055423267 = sum of:
0.055423267 = weight(_text_:22 in 835) [ClassicSimilarity], result of:
0.055423267 = score(doc=835,freq=2.0), product of:
0.17906146 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051133685 = queryNorm
0.30952093 = fieldWeight in 835, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=835)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 29.12.2022 18:22:55
-
Rieger, F.: Lügende Computer (2023)
0.01
0.009237211 = product of:
0.027711634 = sum of:
0.027711634 = product of:
0.055423267 = sum of:
0.055423267 = weight(_text_:22 in 912) [ClassicSimilarity], result of:
0.055423267 = score(doc=912,freq=2.0), product of:
0.17906146 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051133685 = queryNorm
0.30952093 = fieldWeight in 912, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=912)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 16. 3.2023 19:22:55
-
Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023)
0.01
0.0057732575 = product of:
0.017319772 = sum of:
0.017319772 = product of:
0.034639545 = sum of:
0.034639545 = weight(_text_:22 in 1171) [ClassicSimilarity], result of:
0.034639545 = score(doc=1171,freq=2.0), product of:
0.17906146 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051133685 = queryNorm
0.19345059 = fieldWeight in 1171, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1171)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Date
- 23.11.2023 19:07:22
