Search (41 results, page 1 of 3)

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Abstract

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.

Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

Type

a

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Content

Beitrag zur Tagung: "Nationalsozialismus Digital. Die Verantwortung von Bibliotheken, Archiven und Museen sowie Forschungseinrichtungen und Medien im Umgang mit der NSZeit im Netz." Österreichische Nationalbibliothek, Universität Wien, 27. - 29. November 2019

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Abstract

This paper discusses the use of wiki technology to provide a navigation structure for a collection of newspaper clippings. We overview the architecture of the wiki, discuss the navigation structure and pose the question: is the navigation structure an index, and if so, what type, or is it just a linkage structure or topic map. Does such a distinction really matter? Are these definitions in reality function based?

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Date

2. 2.1996 15:40:22

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Date

12. 9.2013 12:29:05

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Abstract

Reports text information retrieval experiments performed as part of the 3 rd round of Text Retrieval Conferences (TREC) using the Okapi online catalogue system at City University, UK. The emphasis in TREC-3 was: further refinement of term weighting functions; an investigation of run time passage determination and searching; expansion of ad hoc queries by terms extracted from the top documents retrieved by a trial search; new methods for choosing query expansion terms after relevance feedback, now split into methods of ranking terms prior to selection and subsequent selection procedures; and the development of a user interface procedure within the new TREC interactive search framework

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Date

1.11.2000 14:55:29

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Abstract

Theory formation is regarded as a process of domain-internal knowledge organization. Misunderstandings about the concept 'theory' are explained. A theory is considered as a systematical representation of a domain realized by three closely related theory-forming actions: establishment of a suitable system of basic concepts, ordering of the experience or given experimental results, synthesizing of conflicting hypotheses. In this view, theory formation means an ambitious kind of knowledge representation. Its consequences are summarized and its importance for the human sciences and for society is emphasized

Date

29. 3.1996 17:26:47

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Abstract

This paper describes the Book House system which is designed to support children's information retrieval in libraries as part of their education. It is a shareware program available on CD-ROM and discs, and comprises functionality for database searching as well as for the classification and storage of book information in the database. The system concept is based on an understanding of children's domain structures and their capabilities for categorization of information needs in connection with their activities in public libraries, in school libraries or in schools. These structures are visualized in the interface by using metaphors and multimedia technology. Through the use of text, images and animation, the Book House supports children - even at a very early age - to learn by doing in an enjoyable way which plays on their previous experiences with computer games. Both words and pictures can be used for searching; this makes the system suitable for all age groups. Even children who have not yet learned to read properly can by selecting pictures search for and find books they would like to have read aloud. Thus at the very beginning of their school period, they can learn to search for books on their own. For the library community itself, such a system will provide an extended service which will increase the number of children's own searches and also improve the relevance, quality and utilization of the collections in the libraries. A market research on the need for an annual indexing service for books in the Book House format is in preparation by the Danish Library Center

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Date

7. 2.1996 22:38:41

Pages

22 S

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Abstract

Logical rules are essential for uncovering the logical connections between relations, which could improve the reasoning performance and provide interpretable results on knowledge graphs (KGs). Although there have been many efforts to mine meaningful logical rules over KGs, existing methods suffer from the computationally intensive searches over the rule space and a lack of scalability for large-scale KGs. Besides, they often ignore the semantics of relations which is crucial for uncovering logical connections. Recently, large language models (LLMs) have shown impressive performance in the field of natural language processing and various applications, owing to their emergent ability and generalizability. In this paper, we propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs to prompt LLMs to generate logical rules. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs. Last, a rule validator harnesses the reasoning ability of LLMs to validate the logical correctness of ranked rules through chain-of-thought reasoning. ChatRule is evaluated on four large-scale KGs, w.r.t. different rule quality metrics and downstream tasks, showing the effectiveness and scalability of our method.

Date

23.11.2023 19:07:22

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Date

17.11.2020 11:29:00

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Footnote

Vgl. auch Beitrag gleichen Titels in: ZfBB 41(1994) H.3, S.297-303

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Abstract

This is a preprint to be published in the Extensions & Corrections to the UDC. The paper explains the basic functions of browsing and searching that need to be supported in relation to analytico-synthetic classifications such as Universal Decimal Classification (UDC), irrespective of any specific, real-life implementation. UDC is an example of a semi-faceted system that can be used, for instance, for both post-coordinate searching and hierarchical/facet browsing. The advantages of using a classification for IR, however, depend on the strength of the GUI, which should provide a user-friendly interface to classification browsing and searching. The power of this interface is in supporting visualisation that will 'convert' what is potentially a user-unfriendly indexing language based on symbols, to a subject presentation that is easy to understand, search and navigate. A summary of the basic functions of searching and browsing a classification that may be provided on a user-friendly interface is given and examples of classification browsing interfaces are provided.

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Abstract

This is a short project that serves as an inspiration for a forthcoming paper, which will explore the technical side of ChatGPT and the ethical issues it presents for academic researchers, which will result in a peer-reviewed publication. This demonstrates that capacities of ChatGPT as a "chatbot" that is far more advanced than many alternatives available today and may even be able to be used to draft entire academic manuscripts for researchers. ChatGPT is available via https://chat.openai.com/chat.

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Abstract

This study considers the expressiveness (that is the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.

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Abstract

A basic distinction in agent-based data-driven Database Semantics (DBS) is between language and nonlanguage cognition. Language cognition transfers content between agents by means of raw data. Nonlanguage cognition maps between content and raw data inside the focus agent. {\it Recognition} applies a concept type to raw data, resulting in a concept token. In language recognition, the focus agent (hearer) takes raw language-data (surfaces) produced by another agent (speaker) as input, while nonlanguage recognition takes raw nonlanguage-data as input. In either case, the output is a content which is stored in the agent's onboard short term memory. {\it Action} adapts a concept type to a purpose, resulting in a token. In language action, the focus agent (speaker) produces language-dependent surfaces for another agent (hearer), while nonlanguage action produces intentions for a nonlanguage purpose. In either case, the output is raw action data. As long as the procedural implementation of place holder values works properly, it is compatible with the DBS requirement of input-output equivalence between the natural prototype and the artificial reconstruction.