The Corpus Thread - Det Danske Sprog- og Litteraturselskab

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6.2. Survey 134 which they have decided not to give open public access to. Access is only given to an online version of the tagger after prior agreement. Neither conditions nor contents of the agreement are accessible on-site. Assessment: poor. Features: Based on Brill’s rule-based framework. POS tagging only. However, CST provides a non-free lemmatizer with restricted access. Assessment: fair. Code: C/C++. Assessment: fair. Architecture: Restricted access to a web version only, not really suited for huge amounts of text. Assessment: poor. Usability: A moderate amount of additional info and a demo is found on the tagger homepage. Assessment: fair. 2. VISL’s Constraint Grammar parser Availability: The VISL CG-3 software has been developed by GrammarSoft and is distributed as open source under the GNU General Public License. However, the Danish grammar DanGram is not publicly available and tagging/parsing of Danish can only be performed via text-by-text upload 23 or through a paid-for remote interface (accessed via the web). Conditions and prices have to be negotiated with GrammarSoft in advance. Assessment: poor. Features: Based on the Constraint Grammar framework (Karlsson et al. (1995)), performs POS tagging, lemmatization and syntactic parsing. It is claimed to have a particularly high precision. Assessment: good. Code: C++. Assessment: fair. Architecture: Restricted access to web-based versions only, not really suited for larger amounts of text. Assessment: poor. Usability: A comprehensive manual, a tutorial, examples, demos, and additional info is found on the homepage. Assessment: good. 6.2.3 Conclusions As availability is considered a major requirement, the following taggers are of particular interest to the WP 2.1 project: Stanford, Apache UIMA, unsupos, Brill, and Sujit Pal’s tagger implementations. Common to all these taggers is that they derive their language model from a training corpus and that they principally work as POS taggers only. The disadvantage of this is that 23 Uploaded texts will be added to VISL’s own corpora if their copyright status permits it.
6.3. Case study 135 lemmatization comes in as a separate process that requires specific tools or extensions to the existing implementations. In addition, unknown words, i.e. words not seen in prior training material, seem to be a problem for all taggers based on learning algorithms that produce language models. However, some of the mentioned taggers may be modified to also take into account lexical knowledge and perform lemmatization as well, in particular Brill and Sujit Pal. Stanford and UIMA may be extendable as well, but their code is rather complex which probably makes the development of extensions difficult. As for unsupos, the unsupervised learning approach probably is not suitable for the needs of WP 2.1. Thus, it emerges that Sujit Pal’s HMM and Brill implementations may be the most attractive solutions to start with. Stanford may be an alternative whereas UIMA seems far too complex for the needs of POS tagging only. It is a pity that both taggers specifically designed for handling Danish have severe usage restrictions, otherwise they might have been worth giving a try as well. The conclusion is to conduct a case study with Sujit Pal’s HMM implementation where it will be trained on the Danish Parole Corpus to evaluate the potential of his HMM approach. If it fails, Stanford can be considered as a fallback option. 6.3 Case study The starting point of the case study is the Java code of Sujit Pal’s HMM implementation 24 including the Java HMM library Jahmm by Jean-Marc François 25 , University of Liège. Sujit Pal’s demo is based on building an HMM from the Brown Corpus; in this study the tagged training corpus is the Danish PAROLE Corpus 26 . 6.3.1 Building a token-based HMM The common approach of modeling an HMM is to view the word forms of a text as visible observations and the set of possible POS tags as hidden states. In the following experiment, this approach, which is also demonstrated on Sujit Pal’s blog, is applied to a setting for Danish. The first step was to convert PAROLE from its TEI-like XML-structure to the necessary Brown input format, as illustrated here: 24 http://sujitpal.blogspot.com/2008/11/ir-math-in-java-hmm-based-pos.html 25 http://www.montefiore.ulg.ac.be/~francois/ 26 http://korpus.dsl.dk/e-resurser/parole-korpus.php?lang=uk
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The Corpus Thread “Reference corp
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Chapter 1 Aim and concepts Sketchin
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1.1. Aim of the project 5 Metadata
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1.2. Project tasks and documentatio
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1.3. Text collection, text bank, co
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Chapter 2 The text bank A platform
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2.2. Implementation 17 and metadata
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Chapter 3 Text metadata What the he
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3.1. Concepts 21 Section 3.3 starts
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3.2. Header structure 23 (file des
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3.2. Header structure 25 great verb
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3.2. Header structure 27 The follow
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3.2. Header structure 29 deprecated
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3.2. Header structure 31 the text i
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3.2. Header structure 35 appDesc
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3.2. Header structure 39 3.2.3.4 Te
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3.3. Filling in the header 41 revi
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Page 175 and 176: Bibliography Andersen, M. S., Asmus
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The Corpus Thread - Det Danske Sprog- og Litteraturselskab

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