Search (14 results, page 1 of 1)

0.12953952 = product of:
  0.19430926 = sum of:
    0.093939 = weight(_text_:search in 1150) [ClassicSimilarity], result of:
      0.093939 = score(doc=1150,freq=8.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.5376164 = fieldWeight in 1150, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1150)
    0.10037026 = product of:
      0.20074052 = sum of:
        0.20074052 = weight(_text_:engines in 1150) [ClassicSimilarity], result of:
          0.20074052 = score(doc=1150,freq=8.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.7858995 = fieldWeight in 1150, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1150)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Voice search is becoming a popular mode for interacting with search engines. As a result, research has gone into building better voice transcription engines, interfaces, and search engines that better handle inherent verbosity of queries. However, when one considers its use by non- native speakers of English, another aspect that becomes important is the formulation of the query by users. In this paper, we present the results of a preliminary study that we conducted with non-native English speakers who formulate queries for given retrieval tasks. Our results show that the current search engines are sensitive in their rankings to the query formulation, and thus highlights the need for developing more robust ranking methods.

0.09297244 = product of:
  0.13945866 = sum of:
    0.08876401 = weight(_text_:search in 2861) [ClassicSimilarity], result of:
      0.08876401 = score(doc=2861,freq=14.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.5079997 = fieldWeight in 2861, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2861)
    0.05069464 = product of:
      0.10138928 = sum of:
        0.10138928 = weight(_text_:engines in 2861) [ClassicSimilarity], result of:
          0.10138928 = score(doc=2861,freq=4.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39693922 = fieldWeight in 2861, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2861)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.

0.026839714 = product of:
  0.08051914 = sum of:
    0.08051914 = weight(_text_:search in 854) [ClassicSimilarity], result of:
      0.08051914 = score(doc=854,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.460814 = fieldWeight in 854, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.09375 = fieldNorm(doc=854)
  0.33333334 = coord(1/3)

Abstract

Siri, Google Search, online marketing and your child's homework will never be the same. Then there's the misinformation problem.

0.015656501 = product of:
  0.0469695 = sum of:
    0.0469695 = weight(_text_:search in 156) [ClassicSimilarity], result of:
      0.0469695 = score(doc=156,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.2688082 = fieldWeight in 156, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0546875 = fieldNorm(doc=156)
  0.33333334 = coord(1/3)

Abstract

In this work, we present the results of research for evaluating a methodology for extracting mathematical terms for precalculus using the techniques for semantically-oriented statistical search. We use the corpus-based approach and the combination of different statistically-based techniques for extracting keywords, collocations and co-occurrences incorporated in the Sketch Engine software. We evaluate the collocations candidate terms for the basic concept function(s) and approve the related methodology by precalculus domain conceptual terms definitions. Finally, we offer a conceptual terms hierarchical representation and discuss the results with respect to their possible applications.

0.013622571 = product of:
  0.040867712 = sum of:
    0.040867712 = product of:
      0.081735425 = sum of:
        0.081735425 = weight(_text_:22 in 4888) [ClassicSimilarity], result of:
          0.081735425 = score(doc=4888,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = queryNorm
            0.46428138 = fieldWeight in 4888, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=4888)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

1. 3.2013 14:56:22

0.013419857 = product of:
  0.04025957 = sum of:
    0.04025957 = weight(_text_:search in 3094) [ClassicSimilarity], result of:
      0.04025957 = score(doc=3094,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.230407 = fieldWeight in 3094, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=3094)
  0.33333334 = coord(1/3)

Abstract

The paper addresses the issue of how online natural language question answering, based on deep semantic analysis, may compete with currently popular keyword search, open domain information retrieval systems, covering a horizontal domain. We suggest the multiagent question answering approach, where each domain is represented by an agent which tries to answer questions taking into account its specific knowledge. The meta-agent controls the cooperation between question answering agents and chooses the most relevant answer(s). We argue that multiagent question answering is optimal in terms of access to business and financial knowledge, flexibility in query phrasing, and efficiency and usability of advice. The knowledge and advice encoded in the system are initially prepared by domain experts. We analyze the commercial application of multiagent question answering and the robustness of the meta-agent. The paper suggests that a multiagent architecture is optimal when a real world question answering domain combines a number of vertical ones to form a horizontal domain.

0.009081715 = product of:
  0.027245143 = sum of:
    0.027245143 = product of:
      0.054490287 = sum of:
        0.054490287 = weight(_text_:22 in 1490) [ClassicSimilarity], result of:
          0.054490287 = score(doc=1490,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = queryNorm
            0.30952093 = fieldWeight in 1490, 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=1490)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 3.2015 9:30:24

0.009081715 = product of:
  0.027245143 = sum of:
    0.027245143 = product of:
      0.054490287 = sum of:
        0.054490287 = weight(_text_:22 in 835) [ClassicSimilarity], result of:
          0.054490287 = score(doc=835,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = 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

0.009081715 = product of:
  0.027245143 = sum of:
    0.027245143 = product of:
      0.054490287 = sum of:
        0.054490287 = weight(_text_:22 in 912) [ClassicSimilarity], result of:
          0.054490287 = score(doc=912,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = 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

0.0089465715 = product of:
  0.026839713 = sum of:
    0.026839713 = weight(_text_:search in 337) [ClassicSimilarity], result of:
      0.026839713 = score(doc=337,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.15360467 = fieldWeight in 337, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.03125 = fieldNorm(doc=337)
  0.33333334 = coord(1/3)

Abstract

Human language is both rich and ambiguous. When we hear or read words, we resolve meanings to mental representations, for example recognizing and linking names to the intended persons, locations or organizations. Bridging words and meaning - from turning search queries into relevant results to suggesting targeted keywords for advertisers - is also Google's core competency, and important for many other tasks in information retrieval and natural language processing. We are happy to release a resource, spanning 7,560,141 concepts and 175,100,788 unique text strings, that we hope will help everyone working in these areas. How do we represent concepts? Our approach piggybacks on the unique titles of entries from an encyclopedia, which are mostly proper and common noun phrases. We consider each individual Wikipedia article as representing a concept (an entity or an idea), identified by its URL. Text strings that refer to concepts were collected using the publicly available hypertext of anchors (the text you click on in a web link) that point to each Wikipedia page, thus drawing on the vast link structure of the web. For every English article we harvested the strings associated with its incoming hyperlinks from the rest of Wikipedia, the greater web, and also anchors of parallel, non-English Wikipedia pages. Our dictionaries are cross-lingual, and any concept deemed too fine can be broadened to a desired level of generality using Wikipedia's groupings of articles into hierarchical categories. The data set contains triples, each consisting of (i) text, a short, raw natural language string; (ii) url, a related concept, represented by an English Wikipedia article's canonical location; and (iii) count, an integer indicating the number of times text has been observed connected with the concept's url. Our database thus includes weights that measure degrees of association. For example, the top two entries for football indicate that it is an ambiguous term, which is almost twice as likely to refer to what we in the US call soccer. Vgl. auch: Spitkovsky, V.I., A.X. Chang: A cross-lingual dictionary for english Wikipedia concepts. In: http://nlp.stanford.edu/pubs/crosswikis.pdf.

0.0089465715 = product of:
  0.026839713 = sum of:
    0.026839713 = weight(_text_:search in 851) [ClassicSimilarity], result of:
      0.026839713 = score(doc=851,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.15360467 = fieldWeight in 851, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.03125 = fieldNorm(doc=851)
  0.33333334 = coord(1/3)

Abstract

Literature review articles are essential to summarize the related work in the selected field. However, covering all related studies takes too much time and effort. This study questions how Artificial Intelligence can be used in this process. We used ChatGPT to create a literature review article to show the stage of the OpenAI ChatGPT artificial intelligence application. As the subject, the applications of Digital Twin in the health field were chosen. Abstracts of the last three years (2020, 2021 and 2022) papers were obtained from the keyword "Digital twin in healthcare" search results on Google Scholar and paraphrased by ChatGPT. Later on, we asked ChatGPT questions. The results are promising; however, the paraphrased parts had significant matches when checked with the Ithenticate tool. This article is the first attempt to show the compilation and expression of knowledge will be accelerated with the help of artificial intelligence. We are still at the beginning of such advances. The future academic publishing process will require less human effort, which in turn will allow academics to focus on their studies. In future studies, we will monitor citations to this study to evaluate the academic validity of the content produced by the ChatGPT. 1. Introduction OpenAI ChatGPT (ChatGPT, 2022) is a chatbot based on the OpenAI GPT-3 language model. It is designed to generate human-like text responses to user input in a conversational context. OpenAI ChatGPT is trained on a large dataset of human conversations and can be used to create responses to a wide range of topics and prompts. The chatbot can be used for customer service, content creation, and language translation tasks, creating replies in multiple languages. OpenAI ChatGPT is available through the OpenAI API, which allows developers to access and integrate the chatbot into their applications and systems. OpenAI ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model developed by OpenAI. It is designed to generate human-like text, allowing it to engage in conversation with users naturally and intuitively. OpenAI ChatGPT is trained on a large dataset of human conversations, allowing it to understand and respond to a wide range of topics and contexts. It can be used in various applications, such as chatbots, customer service agents, and language translation systems. OpenAI ChatGPT is a state-of-the-art language model able to generate coherent and natural text that can be indistinguishable from text written by a human. As an artificial intelligence, ChatGPT may need help to change academic writing practices. However, it can provide information and guidance on ways to improve people's academic writing skills.

0.0078282505 = product of:
  0.02348475 = sum of:
    0.02348475 = weight(_text_:search in 923) [ClassicSimilarity], result of:
      0.02348475 = score(doc=923,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.1344041 = fieldWeight in 923, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.02734375 = fieldNorm(doc=923)
  0.33333334 = coord(1/3)

Abstract

He doesn't dismiss the potential of LLMs to become useful assistants in all sorts of ways-Google and Microsoft have already announced that they will be integrating LLMs into their search and office productivity software. But he talked me through some of his criticisms of the technology's apparent capabilities. At the heart of Dr Marcus's thoughtful critique is an attempt to put LLMs into proper context. Deep learning, the underlying technology that makes LLMs work, is only one piece of the puzzle in the quest for machine intelligence. To reach the level of artificial general intelligence (AGI) that many tech companies strive for-i.e. machines that can plan, reason and solve problems in the way human brains can-they will need to deploy a suite of other AI techniques. These include, for example, the kind of "symbolic AI" that was popular before artificial neural networks and deep learning became all the rage.

0.0067099286 = product of:
  0.020129785 = sum of:
    0.020129785 = weight(_text_:search in 3385) [ClassicSimilarity], result of:
      0.020129785 = score(doc=3385,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.1152035 = fieldWeight in 3385, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3385)
  0.33333334 = coord(1/3)

Content

Mit einem bereits zuvor von Paul Vitanyi und anderen entwickeltem Verfahren, das den Zusammenhang von Objekten misst (normalized information distance - NID ), kann die Nähe zwischen bestimmten Objekten (Bilder, Worte, Muster, Intervalle, Genome, Programme etc.) anhand aller Eigenschaften analysiert und aufgrund der dominanten gemeinsamen Eigenschaft bestimmt werden. Ähnlich können auch die allgemein verwendeten, nicht unbedingt "wahren" Bedeutungen von Namen mit der Google-Suche erschlossen werden. 'At this moment one database stands out as the pinnacle of computer-accessible human knowledge and the most inclusive summary of statistical information: the Google search engine. There can be no doubt that Google has already enabled science to accelerate tremendously and revolutionized the research process. It has dominated the attention of internet users for years, and has recently attracted substantial attention of many Wall Street investors, even reshaping their ideas of company financing.' (Paul Vitanyi und Rudi Cilibrasi) Gibt man ein Wort ein wie beispielsweise "Pferd", erhält man bei Google 4.310.000 indexierte Seiten. Für "Reiter" sind es 3.400.000 Seiten. Kombiniert man beide Begriffe, werden noch 315.000 Seiten erfasst. Für das gemeinsame Auftreten beispielsweise von "Pferd" und "Bart" werden zwar noch immer erstaunliche 67.100 Seiten aufgeführt, aber man sieht schon, dass "Pferd" und "Reiter" enger zusammen hängen. Daraus ergibt sich eine bestimmte Wahrscheinlichkeit für das gemeinsame Auftreten von Begriffen. Aus dieser Häufigkeit, die sich im Vergleich mit der maximalen Menge (5.000.000.000) an indexierten Seiten ergibt, haben die beiden Wissenschaftler eine statistische Größe entwickelt, die sie "normalised Google distance" (NGD) nennen und die normalerweise zwischen 0 und 1 liegt. Je geringer NGD ist, desto enger hängen zwei Begriffe zusammen. "Das ist eine automatische Bedeutungsgenerierung", sagt Vitanyi gegenüber dern New Scientist (4). "Das könnte gut eine Möglichkeit darstellen, einen Computer Dinge verstehen und halbintelligent handeln zu lassen." Werden solche Suchen immer wieder durchgeführt, lässt sich eine Karte für die Verbindungen von Worten erstellen. Und aus dieser Karte wiederum kann ein Computer, so die Hoffnung, auch die Bedeutung der einzelnen Worte in unterschiedlichen natürlichen Sprachen und Kontexten erfassen. So habe man über einige Suchen realisiert, dass ein Computer zwischen Farben und Zahlen unterscheiden, holländische Maler aus dem 17. Jahrhundert und Notfälle sowie Fast-Notfälle auseinander halten oder elektrische oder religiöse Begriffe verstehen könne. Überdies habe eine einfache automatische Übersetzung Englisch-Spanisch bewerkstelligt werden können. Auf diese Weise ließe sich auch, so hoffen die Wissenschaftler, die Bedeutung von Worten erlernen, könne man Spracherkennung verbessern oder ein semantisches Web erstellen und natürlich endlich eine bessere automatische Übersetzung von einer Sprache in die andere realisieren.

0.0045408574 = product of:
  0.013622572 = sum of:
    0.013622572 = product of:
      0.027245143 = sum of:
        0.027245143 = weight(_text_:22 in 4217) [ClassicSimilarity], result of:
          0.027245143 = score(doc=4217,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = queryNorm
            0.15476047 = fieldWeight in 4217, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=4217)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 1.2018 11:32:44