000			05531nam a2200421Ia 4500
005			20250918233800.0
008			140402s2013 caua ab 000 0 eng d
020			_a1608459837
020			_a9781608459834 _cRM123.54
039		9	_a201405201440 _brosli _c201405141307 _dhamudah _y04-02-2014 _zhamudah
040			_aYDXCP _cYDXCP _dBTCTA _dNGU _dWAU _dCIN _dUKM
090			_aP128.C68S333
090			_aP128.C68 _bS333
100	1		_aSchafer, Roland, _d1974-
245	1	0	_aWeb corpus construction / _cRoland Sch✹er and Felix Bildhauer.
260			_a[San Rafael, Calif.] : _bMorgan & Claypool Publishers, _c2013.
300			_axv, 129 p. : _bill. ; _c24 cm.
490	1		_aSynthesis lectures on human language technologies, _x1947-4059 ; _v# 22
500			_a'This volume is a printed version of a work that appears in the Synthesis digital library of engineering and computer science'--P. [4] of cover.
504			_aIncludes bibliographical references (p. 111-128).
505	0		_a1. Web corpora -- 2. Data collection -- 2.1 Introduction -- 2.2 The structure of the web -- 2.2.1 General properties -- 2.2.2 Accessibility and stability of web pages -- 2.2.3 What's in a (national) top level domain? -- 2.2.4 Problematic segments of the web -- 2.3 Crawling basics -- 2.3.1 Introduction -- 2.3.2 Corpus construction from search engine results -- 2.3.3 Crawlers and crawler performance -- 2.3.4 Configuration details and politeness -- 2.3.5 Seed URL generation -- 2.4 More on crawling strategies -- 2.4.1 Introduction -- 2.4.2 Biases and the pagerank -- 2.4.3 Focused crawling -- 3. Post-processing -- 3.1 Introduction -- 3.2 Basic cleanups -- 3.2.1 HTML stripping -- 3.2.2 Character references and entities -- 3.2.3 Character sets and conversion -- 3.2.4 Further normalization -- 3.3 Boilerplate removal -- 3.3.1 Introduction to boilerplate -- 3.3.2 Feature extraction -- 3.3.3 Choice of the machine learning method -- 3.4 Language identification -- 3.5 Duplicate detection -- 3.5.1 Types of duplication -- 3.5.2 Perfect duplicates and hashing -- 3.5.3 Near duplicates, Jaccard coefficients, and shingling -- 4. Linguistic processing -- 4.1 Introduction -- 4.2 Basics of tokenization, part-of-speech tagging, and lemmatization -- 4.2.1 Tokenization -- 4.2.2 Part-of-speech tagging -- 4.2.3 Lemmatization -- 4.3 Linguistic post-processing of noisy data -- 4.3.1 Introduction -- 4.3.2 Treatment of noisy data -- 4.4 Tokenizing web texts -- 4.4.1 Example: missing whitespace -- 4.4.2 Example: emoticons -- 4.5 POS tagging and lemmatization of web texts -- 4.5.1 Tracing back errors in POS tagging -- 4.6 Orthographic normalization -- 4.7 Software for linguistic post-processing -- 5. Corpus evaluation and comparison -- 5.1 Introduction -- 5.2 Rough quality check -- 5.2.1 Word and sentence lengths -- 5.2.2 Duplication -- 5.3 Measuring corpus similarity -- 5.3.1 Inspecting frequency lists -- 5.3.2 Hypothesis testing with -- 5.3.3 Hypothesis testing with Spearman's rank correlation -- 5.3.4 Using test statistics without hypothesis testing -- 5.4 Comparing keywords -- 5.4.1 Keyword extraction with x2 -- 5.4.2 Keyword extraction using the ratio of relative frequencies -- 5.4.3 Variants and refinements -- 5.5 Extrinsic evaluation -- 5.6 Corpus composition -- 5.6.1 Estimating corpus composition -- 5.6.2 Measuring corpus composition -- 5.6.3 Interpreting corpus composition -- 5.7 Summary.
520	3		_aThe World Wide Web constitutes the largest existing source of texts written in a great variety of languages. A feasible and sound way of exploiting this data for linguistic research is to compile a static corpus for a given language. There are several advantages of this approach: (i) Working with such corpora obviates the problems encountered when using Internet search engines in quantitative linguistic research (such as non-transparent ranking algorithms). (ii) Creating a corpus from web data is virtually free. (iii) The size of corpora compiled from the WWW may exceed by several orders of magnitudes the size of language resources offered elsewhere. (iv) The data is locally available to the user, and it can be linguistically post-processed and queried with the tools preferred by her/him. This book addresses the main practical tasks in the creation of web corpora up to giga-token size. Among these tasks are the sampling process (i. e., web crawling) and the usual cleanups including boilerplate removal and removal of duplicated content. Linguistic processing and problems with linguistic processing coming from the different kinds of noise in web corpora are also covered. Finally, the authors show how web corpora can be evaluated and compared to other corpora (such as traditionally compiled corpora).
650		0	_aCorpora (Linguistics) _xData processing.
650		0	_aComputational linguistics.
650		0	_aWeb search engines.
700	1		_aBildhauer, Felix.
710	2		_aMorgan & Claypool Publishers.
730	0		_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on human language technologies, _x1947-4059 ; _v# 22
907			_a.b15863700 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kP128.C68S333
914			_avtls003555304
990			_ark4
991			_aFakulti Sains dan Teknologi Maklumat
998			_at _b2014-02-04 _cm _da _feng _gcau _y0 _z.b15863700
999			_c566200 _d566200