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Web corpus construction
The 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 su...
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| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
c2013.
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| Series: | Synthesis digital library of engineering and computer science.
Synthesis lectures on human language technologies ; # 22. |
| Subjects: | |
| Online Access: | Abstract with links to full text |
| LEADER | 06518nam a2200769 a 4500 | ||
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| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 130814s2013 caua foab 000 0 eng d | ||
| 020 | |a 9781608459841 (electronic bk.) | ||
| 020 | |z 9781608459834 (pbk.) | ||
| 024 | 7 | |a 10.2200/S00508ED1V01Y201305HLT022 |2 doi | |
| 035 | |a (CaBNVSL)swl00402651 | ||
| 035 | |a (OCoLC)855857901 | ||
| 040 | |a CaBNVSL |c CaBNVSL |d CaBNVSL | ||
| 050 | 4 | |a P128.C68 |b S358 2013 | |
| 082 | 0 | 4 | |a 410.188 |2 23 |
| 100 | 1 | |a Sch�afer, Roland. | |
| 245 | 1 | 0 | |a Web corpus construction |h [electronic resource] / |c Roland Sch�afer and Felix Bildhauer. |
| 260 | |a San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : |b Morgan & Claypool, |c c2013. | ||
| 300 | |a 1 electronic text (xv, 129 p.) : |b ill., digital file. | ||
| 490 | 1 | |a Synthesis lectures on human language technologies, |x 1947-4059 ; |v # 22 | |
| 500 | |a Part of: Synthesis digital library of engineering and computer science. | ||
| 500 | |a Series from website. | ||
| 504 | |a Includes bibliographical references (p. 111-128). | ||
| 505 | 0 | |a 1. Web corpora -- | |
| 505 | 8 | |a 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 -- | |
| 505 | 8 | |a 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 -- | |
| 505 | 8 | |a 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 -- | |
| 505 | 8 | |a 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 -- | |
| 505 | 8 | |a Bibliography -- Authors' biographies. | |
| 506 | |a Abstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | |a Compendex | |
| 510 | 0 | |a Google book search | |
| 510 | 0 | |a Google scholar | |
| 510 | 0 | |a INSPEC | |
| 520 | 3 | |a The 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). | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 538 | |a System requirements: Adobe Acrobat Reader. | ||
| 588 | |a Title from PDF t.p. (viewed on August 14, 2013). | ||
| 650 | 0 | |a Computational linguistics. | |
| 650 | 0 | |a Corpora (Linguistics) |x Data processing. | |
| 650 | 0 | |a Web search engines. | |
| 653 | |a boilerplate removal | ||
| 653 | |a corpus comparison | ||
| 653 | |a corpus creation | ||
| 653 | |a corpus evaluation | ||
| 653 | |a duplicate detection | ||
| 653 | |a keyword extraction | ||
| 653 | |a language identification | ||
| 653 | |a near-duplicate detection | ||
| 653 | |a noisy data | ||
| 653 | |a POS tagging | ||
| 653 | |a tokenization | ||
| 653 | |a web characterization | ||
| 653 | |a web corpora | ||
| 653 | |a web crawling | ||
| 700 | 1 | |a Bildhauer, Felix. | |
| 776 | 0 | 8 | |i Print version: |z 9781608459834 |
| 830 | 0 | |a Synthesis digital library of engineering and computer science. | |
| 830 | 0 | |a Synthesis lectures on human language technologies ; |v # 22. |x 1947-4059 | |
| 856 | 4 | 8 | |3 Abstract with links to full text |u http://dx.doi.org/10.2200/S00508ED1V01Y201305HLT022 |
| 942 | |c EB | ||
| 999 | |c 81070 |d 81070 | ||
| 952 | |0 0 |1 0 |4 0 |7 0 |9 73090 |a MGUL |b MGUL |d 2016-03-20 |l 0 |r 2016-03-20 |w 2016-03-20 |y EB | ||