Daniël de Kok

Bio: Daniël de Kok is an academic researcher from University of Tübingen. The author has contributed to research in topics: Parsing & Treebank. The author has an hindex of 8, co-authored 24 publications receiving 430 citations. Previous affiliations of Daniël de Kok include University of Groningen.

Large Scale Syntactic Annotation of written Dutch (LASSY)

Abstract: This chapter presents the Lassy Small and Lassy Large treebanks, as well as related tools and applications. Lassy Small is a corpus of written Dutch texts (1,000,000 words) which has been syntactically annotated with manual verification and correction. Lassy Large is a much larger corpus (over 500,000,000 words) which has been syntactically annotated fully automatically. In addition, various browse and search tools for syntactically annotated corpora have been developed and made available. Their potential for applications in corpus linguistics and information extraction has been illustrated and evaluated in a series of case studies.

Reversible Stochastic Attribute-Value Grammars

Abstract: An attractive property of attribute-value grammars is their reversibility. Attribute-value grammars are usually coupled with separate statistical components for parse selection and fluency ranking. We propose reversible stochastic attribute-value grammars, in which a single statistical model is employed both for parse selection and fluency ranking.

A generalized method for iterative error mining in parsing results

Abstract: Error mining is a useful technique for identifying forms that cause incomplete parses of sentences. We extend the iterative method of Sagot and de la Clergerie (2006) to treat n-grams of an arbitrary length. An inherent problem of incorporating longer n-grams is data sparseness. Our new method takes sparseness into account, producing n-grams that are as long as necessary to identify problematic forms, but not longer. Not every cause for parsing errors can be captured effectively by looking at word n-grams. We report on an algorithm for building more general patterns for mining, consisting of words and part of speech tags. It is not easy to evaluate the various error mining techniques. We propose a new evaluation metric which will enable us to compare different error miners.

Improving efficiency and accuracy in multilingual entity extraction

Abstract: There has recently been an increased interest in named entity recognition and disambiguation systems at major conferences such as WWW, SIGIR, ACL, KDD, etc. However, most work has focused on algorithms and evaluations, leaving little space for implementation details. In this paper, we discuss some implementation and data processing challenges we encountered while developing a new multilingual version of DBpedia Spotlight that is faster, more accurate and easier to configure. We compare our solution to the previous system, considering time performance, space requirements and accuracy in the context of the Dutch and English languages. Additionally, we report results for 9 additional languages among the largest Wikipedias. Finally, we present challenges and experiences to foment the discussion with other developers interested in recognition and disambiguation of entities in natural language text.

The ACL anthology network corpus

Abstract: We introduce the ACL Anthology Network (AAN), a comprehensive manually curated networked database of citations, collaborations, and summaries in the field of Computational Linguistics. We also present a number of statistics about the network including the most cited authors, the most central collaborators, as well as network statistics about the paper citation, author citation, and author collaboration networks.

Parsing Argumentation Structures in Persuasive Essays

Abstract: In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecti...