Search (35 results, page 1 of 2)

0.03121005 = product of:
  0.10923517 = sum of:
    0.025441473 = weight(_text_:computer in 1789) [ClassicSimilarity], result of:
      0.025441473 = score(doc=1789,freq=10.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.18057145 = fieldWeight in 1789, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.08379369 = sum of:
      0.07334675 = weight(_text_:aufsatzsammlung in 1789) [ClassicSimilarity], result of:
        0.07334675 = score(doc=1789,freq=8.0), product of:
          0.25295308 = queryWeight, product of:
            6.5610886 = idf(docFreq=169, maxDocs=44218)
            0.038553525 = queryNorm
          0.28996187 = fieldWeight in 1789, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            6.5610886 = idf(docFreq=169, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
      0.010446941 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
        0.010446941 = score(doc=1789,freq=2.0), product of:
          0.13500787 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.038553525 = queryNorm
          0.07738023 = fieldWeight in 1789, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
  0.2857143 = coord(2/7)

Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

RSWK

Data Mining / Visualisierung / Aufsatzsammlung (BVB)
Wissensextraktion / Visualisierung / Aufsatzsammlung (BVB)

Subject

Data Mining / Visualisierung / Aufsatzsammlung (BVB)
Wissensextraktion / Visualisierung / Aufsatzsammlung (BVB)

0.029356267 = product of:
  0.10274693 = sum of:
    0.050882947 = weight(_text_:computer in 362) [ClassicSimilarity], result of:
      0.050882947 = score(doc=362,freq=10.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.3611429 = fieldWeight in 362, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.03125 = fieldNorm(doc=362)
    0.051863987 = product of:
      0.103727974 = sum of:
        0.103727974 = weight(_text_:aufsatzsammlung in 362) [ClassicSimilarity], result of:
          0.103727974 = score(doc=362,freq=4.0), product of:
            0.25295308 = queryWeight, product of:
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.038553525 = queryNorm
            0.41006804 = fieldWeight in 362, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.03125 = fieldNorm(doc=362)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

LCSH

Computer science
Computer Communication Networks

RSWK

Text Mining / Aufsatzsammlung

Subject

Text Mining / Aufsatzsammlung
Computer science
Computer Communication Networks

0.024466056 = product of:
  0.17126238 = sum of:
    0.17126238 = sum of:
      0.15559196 = weight(_text_:aufsatzsammlung in 1833) [ClassicSimilarity], result of:
        0.15559196 = score(doc=1833,freq=16.0), product of:
          0.25295308 = queryWeight, product of:
            6.5610886 = idf(docFreq=169, maxDocs=44218)
            0.038553525 = queryNorm
          0.61510205 = fieldWeight in 1833, product of:
            4.0 = tf(freq=16.0), with freq of:
              16.0 = termFreq=16.0
            6.5610886 = idf(docFreq=169, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
      0.015670411 = weight(_text_:22 in 1833) [ClassicSimilarity], result of:
        0.015670411 = score(doc=1833,freq=2.0), product of:
          0.13500787 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.038553525 = queryNorm
          0.116070345 = fieldWeight in 1833, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
  0.14285715 = coord(1/7)

Date

11. 5.2008 19:49:22

RSWK

Mediendokumentation / Aufsatzsammlung
Medien / Informationsmanagement / Aufsatzsammlung
Pressearchiv / Aufsatzsammlung (HBZ)
Rundfunkarchiv / Aufsatzsammlung (HBZ)

Subject

Mediendokumentation / Aufsatzsammlung
Medien / Informationsmanagement / Aufsatzsammlung
Pressearchiv / Aufsatzsammlung (HBZ)
Rundfunkarchiv / Aufsatzsammlung (HBZ)

0.016601244 = product of:
  0.05810435 = sum of:
    0.039822206 = weight(_text_:computer in 2908) [ClassicSimilarity], result of:
      0.039822206 = score(doc=2908,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.28263903 = fieldWeight in 2908, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2908)
    0.018282145 = product of:
      0.03656429 = sum of:
        0.03656429 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.03656429 = score(doc=2908,freq=2.0), product of:
            0.13500787 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038553525 = queryNorm
            0.2708308 = fieldWeight in 2908, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2908)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation

Source

Information systems. 22(1997) nos.5/6, S.349-385

0.009752377 = product of:
  0.06826664 = sum of:
    0.06826664 = weight(_text_:computer in 977) [ClassicSimilarity], result of:
      0.06826664 = score(doc=977,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.48452407 = fieldWeight in 977, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.09375 = fieldNorm(doc=977)
  0.14285715 = coord(1/7)

0.009752377 = product of:
  0.06826664 = sum of:
    0.06826664 = weight(_text_:computer in 2301) [ClassicSimilarity], result of:
      0.06826664 = score(doc=2301,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.48452407 = fieldWeight in 2301, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.09375 = fieldNorm(doc=2301)
  0.14285715 = coord(1/7)

0.0092614265 = product of:
  0.06482998 = sum of:
    0.06482998 = product of:
      0.12965997 = sum of:
        0.12965997 = weight(_text_:aufsatzsammlung in 804) [ClassicSimilarity], result of:
          0.12965997 = score(doc=804,freq=4.0), product of:
            0.25295308 = queryWeight, product of:
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.038553525 = queryNorm
            0.51258504 = fieldWeight in 804, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.0390625 = fieldNorm(doc=804)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

RSWK

Text Mining / Aufsatzsammlung

Subject

Text Mining / Aufsatzsammlung

0.0092614265 = product of:
  0.06482998 = sum of:
    0.06482998 = product of:
      0.12965997 = sum of:
        0.12965997 = weight(_text_:aufsatzsammlung in 4019) [ClassicSimilarity], result of:
          0.12965997 = score(doc=4019,freq=4.0), product of:
            0.25295308 = queryWeight, product of:
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.038553525 = queryNorm
            0.51258504 = fieldWeight in 4019, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.5610886 = idf(docFreq=169, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4019)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

RSWK

Text Mining / Aufsatzsammlung

Subject

Text Mining / Aufsatzsammlung

0.006501585 = product of:
  0.045511093 = sum of:
    0.045511093 = weight(_text_:computer in 6759) [ClassicSimilarity], result of:
      0.045511093 = score(doc=6759,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.32301605 = fieldWeight in 6759, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=6759)
  0.14285715 = coord(1/7)

Abstract

Examines the relationship between systems and users from the knowledge discovery in databases or data mining perspecitives. A comprehensive study on knowledge discovery in human computer symbiosis is needed. Proposes a database-user adversarial partnership, which is general enough to cover knowledge discovery and security of issues related to databases and their users. It can be further generalized into system-user adversarial paertnership. Discusses opportunities provided by knowledge discovery techniques and potential social implications

0.006501585 = product of:
  0.045511093 = sum of:
    0.045511093 = weight(_text_:computer in 3822) [ClassicSimilarity], result of:
      0.045511093 = score(doc=3822,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.32301605 = fieldWeight in 3822, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=3822)
  0.14285715 = coord(1/7)

Series

Lecture notes in computer science; vol.1510

0.0057466435 = product of:
  0.040226504 = sum of:
    0.040226504 = weight(_text_:computer in 2931) [ClassicSimilarity], result of:
      0.040226504 = score(doc=2931,freq=4.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.28550854 = fieldWeight in 2931, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2931)
  0.14285715 = coord(1/7)

Abstract

Die rasante technologische Entwicklung der letzten zwei Jahrzehnte ermöglicht heute die persistente Speicherung riesiger Datenmengen durch den Computer. Forscher an der Universität Berkeley haben berechnet, dass jedes Jahr ca. 1 Exabyte (= 1 Million Terabyte) Daten generiert werden - ein großer Teil davon in digitaler Form. Das bedeutet aber, dass in den nächsten drei Jahren mehr Daten generiert werden als in der gesamten menschlichen Entwicklung zuvor. Die Daten werden oft automatisch mit Hilfe von Sensoren und Überwachungssystemen aufgezeichnet. So werden beispielsweise alltägliche Vorgänge des menschlichen Lebens, wie das Bezahlen mit Kreditkarte oder die Benutzung des Telefons, durch Computer aufgezeichnet. Dabei werden gewöhnlich alle verfügbaren Parameter abgespeichert, wodurch hochdimensionale Datensätze entstehen. Die Daten werden gesammelt, da sie wertvolle Informationen enthalten, die einen Wettbewerbsvorteil bieten können. Das Finden der wertvollen Informationen in den großen Datenmengen ist aber keine leichte Aufgabe. Heutige Datenbankmanagementsysteme können nur kleine Teilmengen dieser riesigen Datenmengen darstellen. Werden die Daten zum Beispiel in textueller Form ausgegeben, können höchstens ein paar hundert Zeilen auf dem Bildschirm dargestellt werden. Bei Millionen von Datensätzen ist dies aber nur ein Tropfen auf den heißen Stein.

0.0056888866 = product of:
  0.039822206 = sum of:
    0.039822206 = weight(_text_:computer in 3821) [ClassicSimilarity], result of:
      0.039822206 = score(doc=3821,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.28263903 = fieldWeight in 3821, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3821)
  0.14285715 = coord(1/7)

Series

Lecture notes in computer science; vol.1574

0.0052234703 = product of:
  0.03656429 = sum of:
    0.03656429 = product of:
      0.07312858 = sum of:
        0.07312858 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.07312858 = score(doc=4577,freq=2.0), product of:
            0.13500787 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038553525 = queryNorm
            0.5416616 = fieldWeight in 4577, 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=4577)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

2. 4.2000 18:01:22

0.0048761885 = product of:
  0.03413332 = sum of:
    0.03413332 = weight(_text_:computer in 1611) [ClassicSimilarity], result of:
      0.03413332 = score(doc=1611,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.24226204 = fieldWeight in 1611, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=1611)
  0.14285715 = coord(1/7)

Abstract

"Garbage in, garbage out" is a well-known phrase in computer analysis, and one that comes to mind when mining Web data to draw conclusions about Web users. The challenge is that data analysts wish to infer patterns of client-side behavior from server-side data. However, because only a fraction of the user's actions ever reaches the Web server, analysts must rely an incomplete data. In this paper, we propose a client-side monitoring system that is unobtrusive and supports flexible data collection. Moreover, the proposed framework encompasses client-side applications beyond the Web browser. Expanding monitoring beyond the browser to incorporate standard office productivity tools enables analysts to derive a much richer and more accurate picture of user behavior an the Web.

0.0048761885 = product of:
  0.03413332 = sum of:
    0.03413332 = weight(_text_:computer in 5214) [ClassicSimilarity], result of:
      0.03413332 = score(doc=5214,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.24226204 = fieldWeight in 5214, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=5214)
  0.14285715 = coord(1/7)

Abstract

To prevent type-one error, statisticians tend to accept the possibility of type-two error, which leads to the rejection of hypotheses later shown to be true. In both Exploratory Data Analysis and data mining the emphasis is more appropriately on the elimination of type-two error. Thus EDA methods, including its visualization tools may be appropriate for Data Mining. Seriation, creates a matrix of observations and variables, where the cells contain an icon whose size represents its value, and permits the movement of rows and columns in order to visually discern patterns. Augmented Seriation, a method of data mining, adds computer graphics, sound, color, and extra dimensions to the matrix so that the analyst has different modalities for pattern observation. Gluck has developed software for such analysis.

0.0048761885 = product of:
  0.03413332 = sum of:
    0.03413332 = weight(_text_:computer in 1067) [ClassicSimilarity], result of:
      0.03413332 = score(doc=1067,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.24226204 = fieldWeight in 1067, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=1067)
  0.14285715 = coord(1/7)

Abstract

In this paper we combine computer vision and data mining techniques to model high-level concepts for image retrieval, on the basis of basic perceptual features of the human visual system. High-level concepts related to these features are learned and represented by means of a set of fuzzy association rules. The concepts so acquired can be used for image retrieval with the advantage that it is not needed to provide an image as a query. Instead, a query is formulated by using the labels that identify the learned concepts as search terms, and the retrieval process calculates the relevance of an image to the query by an inference mechanism. An additional feature of our methodology is that it can capture user's subjectivity. For that purpose, fuzzy sets theory is employed to measure user's assessments about the fulfillment of a concept by an image.

0.0048761885 = product of:
  0.03413332 = sum of:
    0.03413332 = weight(_text_:computer in 3015) [ClassicSimilarity], result of:
      0.03413332 = score(doc=3015,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.24226204 = fieldWeight in 3015, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=3015)
  0.14285715 = coord(1/7)

Abstract

We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use-both individually and collectively-over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.

0.0048761885 = product of:
  0.03413332 = sum of:
    0.03413332 = weight(_text_:computer in 720) [ClassicSimilarity], result of:
      0.03413332 = score(doc=720,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.24226204 = fieldWeight in 720, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
  0.14285715 = coord(1/7)

Abstract

This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.

0.0044772606 = product of:
  0.031340823 = sum of:
    0.031340823 = product of:
      0.062681645 = sum of:
        0.062681645 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.062681645 = score(doc=6783,freq=2.0), product of:
            0.13500787 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038553525 = queryNorm
            0.46428138 = fieldWeight in 6783, 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=6783)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

0.004063491 = product of:
  0.028444434 = sum of:
    0.028444434 = weight(_text_:computer in 5083) [ClassicSimilarity], result of:
      0.028444434 = score(doc=5083,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.20188503 = fieldWeight in 5083, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5083)
  0.14285715 = coord(1/7)

Series

Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence