MARC details
| 000 -LEADER |
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| fixed length control field |
06642nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210112b2018 a|||f mb|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
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| Language code of text |
eng |
| Language code of abstract |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Shaimaa Abdelber Shamseldin Ali |
| 245 1# - TITLE STATEMENT |
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| Title |
Text Auto-Tagging Using Wikipiedia / |
| Statement of responsibility, etc. |
Shaimaa Abdelber Shamseldin Ali |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
95 p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: Samhaa El-Beltagy |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2018 . |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents:<br/>Chapter 1: Introduction ................................................................................... 1<br/>1.1 Motivation…………. ..................................................................................... 1<br/>1.2 Problem definition………………. ................................................................ 2<br/>1.3 Contributions. …………….. .......................................................................... 2<br/>1.4 Thesis outline ………………. ....................................................................... 3<br/>Chapter 2: Background ................................................................................... 4<br/>2.1 Wikipedia…………….. ................................................................................. 4<br/>2.2 Text mining…………….. .............................................................................. 5<br/>2.2.1 Text mining application…………….. ................................................. 6<br/>2.2.2 Text mining pre-processing …………….. ........................................... 7<br/>2.2.3 Information Retrieval (IR) …………….. ............................................ 8<br/>2.2.4 Word Sense Disambiguation (WSD) …………….. .......................... 11<br/>2.3 Measuring semantic relatedness…………….. ............................................. 12<br/>2.3.1 Cosine Similarity…………….. ........................................................ 12<br/>2.3.2 The Jaccard Cofficient…………….. ................................................ 13<br/>2.3.3 Milne and Witten’s Wikipedia Link-based Measure (WLM)…………….. ........................................................................ 14<br/>2.4 Information retrieval evaluation measures…………….. ............................. 17<br/>Chapter 3: Related Work .............................................................................. 19<br/>3.1 Wikify! Linking Documents to Encyclopedia knowledge ......................... 19<br/>3.2 Learning to Link with Wikipedia .............................................................. 25<br/>3.3 Fast and accurate annotation of short text with Wikipedia pages ............. 30<br/>Table of Contents<br/>v<br/>3.3.1 Information Stored ..................................................................... 31<br/>3.3.2 Algorithm Applied ..................................................................... 32<br/>3.4 A model for Auto-Tagging of Research Papers based on Keyphrase Extraction Methods ................................................................................... 37<br/>Chapter 4: Design and Implementation ........................................................ 39<br/>4.1 Design objective ......................................................................................... 39<br/>4.2 The proposed approach .............................................................................. 40<br/>4.2.1 Phase 1: Building the concept dictionary ...................................... 41<br/>4.2.1.1 Extract needed information from Wikipedia and carry out processing on it ............................................................... 41<br/>4.2.1.2 Perform entry filtration ................................................... 52<br/>4.2.1.3 Measure semantic relatedness .......................................... 53<br/>4.2.1.4 Build an inverted index of dictionary entries .................. 56<br/>4.2.1.5 Perform entry partitioning ................................................ 57<br/>4.2.2 Phase 2: Tagging input text........................................................... 58<br/>Chapter 5: Evaluation ................................................................................... 68<br/>5.1 Building the evaluation dataset .................................................................. 68<br/>5.2 Result ........................................................................................................ 71<br/>5.3 Conclusion ................................................................................................ 71<br/>Chapter 6: Conclusion and Future Work ...................................................... 73<br/>6.1 Summary and Conclusion .......................................................................... 73<br/>6.2 Future Work ............................................................................................... 74<br/>List of Abbreviations ..................................................................................... 76<br/>References ...................................................................................................... |
| 520 3# - Abstract |
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| Abstract |
Abstract:<br/>Because of large amounts of unstructured text data generated on the Internet, Text mining is believed to have high opportunity to significant developments. An important goal of text mining is to sift through large volumes of text to extract patterns and models that can then be incorporated in intelligent applications, such as automatic text categorizers and named entity recognition. This dissertation proposes an efficient method for automatically annotating Arabic news stories with tags using Wikipedia. The idea of the system is to use Wikipedia article names, properties, and re-directs to build a pool of meaningful tags. Sophisticated and efficient matching methods are then used to detect text fragments in input news stories that correspond to entries in the constructed tag pool. Generated tags represent real life entities or concepts such as the names of popular places, known organizations, celebrities, etc. These tags can be used indirectly by a news site for indexing, clustering, classification, statistics generation or directly to give a news reader an overview of news story contents. Evaluation of the system has shown that the tags it generates are better than those generated by MSN Arabic news. |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English. |
| 650 #4 - Subject |
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| Subject |
Informatics-IFM |
| 655 #7 - Index Term-Genre/Form |
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| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
Informatics-IFM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 650 #4 - Subject |
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| -- |
266 |
| 655 #7 - Index Term-Genre/Form |
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| -- |
187 |
| 690 ## - Subject |
|---|
| -- |
266 |