-
Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004)
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- Content
- Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
- Date
- 8. 1.2013 10:22:32
-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
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- Source
- https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
-
Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012)
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- Content
- A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
- Date
- 10. 1.2013 19:22:47
-
Fóris, A.: Network theory and terminology (2013)
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- Abstract
- The paper aims to present the relations of network theory and terminology. The model of scale-free networks, which has been recently developed and widely applied since, can be effectively used in terminology research as well. Operation based on the principle of networks is a universal characteristic of complex systems. Networks are governed by general laws. The model of scale-free networks can be viewed as a statistical-probability model, and it can be described with mathematical tools. Its main feature is that "everything is connected to everything else," that is, every node is reachable (in a few steps) starting from any other node; this phenomena is called "the small world phenomenon." The existence of a linguistic network and the general laws of the operation of networks enable us to place issues of language use in the complex system of relations that reveal the deeper connection s between phenomena with the help of networks embedded in each other. The realization of the metaphor that language also has a network structure is the basis of the classification methods of the terminological system, and likewise of the ways of creating terminology databases, which serve the purpose of providing easy and versatile accessibility to specialised knowledge.
- Date
- 2. 9.2014 21:22:48
-
Ruge, G.: Sprache und Computer : Wortbedeutung und Termassoziation. Methoden zur automatischen semantischen Klassifikation (1995)
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- Content
- Enthält folgende Kapitel: (1) Motivation; (2) Language philosophical foundations; (3) Structural comparison of extensions; (4) Earlier approaches towards term association; (5) Experiments; (6) Spreading-activation networks or memory models; (7) Perspective. Appendices: Heads and modifiers of 'car'. Glossary. Index. Language and computer. Word semantics and term association. Methods towards an automatic semantic classification
- Footnote
- Rez. in: Knowledge organization 22(1995) no.3/4, S.182-184 (M.T. Rolland)
-
Hofstadter, D.: Artificial neural networks today are not conscious (2022)
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- Content
- Vgl. auch: Agüera y Arcas, B.: Artificial neural networks are making strides towards consciousness..
- Source
- ¬The Economist. 2022, [https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-today-are-not-conscious-according-to-douglas-hofstadter?giftId=81ea03d7-78f3-4e84-8824-6aa9dac9ab01]
-
Agüera y Arcas, B.: Artificial neural networks are making strides towards consciousness (2022)
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- Content
- Vgl. auch: Hofstadter, D.: Artificial neural networks today are not conscious.
- Source
- ¬The Economist. 2022, [https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas?giftId=89e08696-9884-4670-b164-df58fffdf067]
-
Griffiths, T.L.; Steyvers, M.: ¬A probabilistic approach to semantic representation (2002)
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- Abstract
- Semantic networks produced from human data have statistical properties that cannot be easily captured by spatial representations. We explore a probabilistic approach to semantic representation that explicitly models the probability with which words occurin diffrent contexts, and hence captures the probabilistic relationships between words. We show that this representation has statistical properties consistent with the large-scale structure of semantic networks constructed by humans, and trace the origins of these properties.
-
Moisl, H.: Artificial neural networks and Natural Language Processing (2009)
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- Abstract
- This entry gives an overview of work to date on natural language processing (NLP) using artificial neural networks (ANN). It is in three main parts: the first gives a brief introduction to ANNs, the second outlines some of the main issues in ANN-based NLP, and the third surveys specific application areas. Each part cites a representative selection of research literature that itself contains pointers to further reading.
-
Warner, A.J.: Natural language processing (1987)
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- Source
- Annual review of information science and technology. 22(1987), S.79-108
-
Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024)
0.02
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- Abstract
- Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
-
Sabah, G.: Knowledge representation and natural language understanding (1993)
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- Abstract
- Describes the basic artificial intelligence techniques in linguistic knowledge processing which attempts to get machines to understand natural languages. Focusses on how computing techniques can model the communication process. Briefly examines the theoretical and practical importance of this field. Introduces a sample of theories used to represent linguistic knowledge. Present semantic representations (various logics and semantic networks) and examines pragmatic aspects of communication (of discourse analysis). Describes parsing systems. Addresses architectural issues. Shows why Distributed Artificial Intelligence and reflective systems offers the best framework taking examples from the CARAMEL (Comprehension Automatique de Recites, Apprentissage et Modelisation des Exchanges langagiers)
-
McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996)
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- Source
- Computational linguistics. 22(1996) no.2, S.217-248
-
Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991)
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- Date
- 8.10.2000 11:52:22
-
Somers, H.: Example-based machine translation : Review article (1999)
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- Date
- 31. 7.1996 9:22:19
-
New tools for human translators (1997)
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- Date
- 31. 7.1996 9:22:19
-
Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997)
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- Date
- 28. 2.1999 10:48:22
-
¬Der Student aus dem Computer (2023)
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- Date
- 27. 1.2023 16:22:55
-
Suissa, O.; Elmalech, A.; Zhitomirsky-Geffet, M.: Text analysis using deep neural networks in digital humanities and information science (2022)
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- 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.
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Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001)
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- Abstract
- This paper presents a method that exploits the hierarchical structure of an indexing vocabulary to guide the development and training of machine learning methods for automatic text categorization. We present the design of a hierarchical classifier based an the divide-and-conquer principle. The method is evaluated using backpropagation neural networks, such as the machine learning algorithm, that leam to assign MeSH categories to a subset of MEDLINE records. Comparisons with traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers, are provided. The results indicate that the use of hierarchical structures improves Performance significantly.
