Jaap Kamps


2022

pdf bib
Entity Linking in the ParlaMint Corpus
Ruben van Heusden | Maarten Marx | Jaap Kamps
Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference

The ParlaMint corpus is a multilingual corpus consisting of the parliamentary debates of seventeen European countries over a span of roughly five years. The automatically annotated versions of these corpora provide us with a wealth of linguistic information, including Named Entities. In order to further increase the research opportunities that can be created with this corpus, the linking of Named Entities to a knowledge base is a crucial step. If this can be done successfully and accurately, a lot of additional information can be gathered from the entities, such as political stance and party affiliation, not only within countries but also between the parliaments of different countries. However, due to the nature of the ParlaMint dataset, this entity linking task is challenging. In this paper, we investigate the task of linking entities from ParlaMint in different languages to a knowledge base, and evaluating the performance of three entity linking methods. We will be using DBPedia spotlight, WikiData and YAGO as the entity linking tools, and evaluate them on local politicians from several countries. We discuss two problems that arise with the entity linking in the ParlaMint corpus, namely inflection, and aliasing or the existence of name variants in text. This paper provides a first baseline on entity linking performance on multiple multilingual parliamentary debates, describes the problems that occur when attempting to link entities in ParlaMint, and makes a first attempt at tackling the aforementioned problems with existing methods.

2020

pdf bib
Who mentions whom? Recognizing political actors in proceedings
Lennart Kerkvliet | Jaap Kamps | Maarten Marx
Proceedings of the Second ParlaCLARIN Workshop

We show that it is straightforward to train a state of the art named entity tagger (spaCy) to recognize political actors in Dutch parliamentary proceedings with high accuracy. The tagger was trained on 3.4K manually labeled examples, which were created in a modest 2.5 days work. This resource is made available on github. Besides proper nouns of persons and political parties, the tagger can recognize quite complex definite descriptions referring to cabinet ministers, ministries, and parliamentary committees. We also provide a demo search engine which employs the tagged entities in its SERP and result summaries.

2007

pdf bib
Deriving a Domain Specific Test Collection from a Query Log
Avi Arampatzis | Jaap Kamps | Marijn Koolen | Nir Nussbaum
Proceedings of the Workshop on Language Technology for Cultural Heritage Data (LaTeCH 2007).

2004

pdf bib
Using WordNet to Measure Semantic Orientations of Adjectives
Jaap Kamps | Maarten Marx | Robert J. Mokken | Maarten de Rijke
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)