Freedom of Information Act (FOIA) legislation grants citizens the right to request information from various levels of the government, and aims to promote the transparency of governmental agencies. However, the processing of these requests is often met with delays, due to the inherent complexity of gathering the required documents. To obtain accurate estimates of the processing times of requests, and to identify bottlenecks in the process, this research proposes a pipeline to automatically extract these timelines from decision letters of Dutch FOIA requests. These decision letters are responses to requests, and contain an overview of the process, including when the request was received, and possible communication between the requester and the relevant agency. The proposed pipeline can extract dates with an accuracy of .94, extract event phrases with a mean ROUGE- L F1 score of .80 and can classify events with a macro F1 score of .79.Out of the 50 decision letters used for testing (each letter containing one timeline), the model correctly classified 10 of the timelines completely correct, with an average of 3.1 mistakes per decision letter.

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.

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.

A corpus called DutchParl is created which aims to contain all digitally available parliamentary documents written in the Dutch language. The first version of DutchParl contains documents from the parliaments of The Netherlands, Flanders and Belgium. The corpus is divided along three dimensions: per parliament, scanned or digital documents, written recordings of spoken text and others. The digital collection contains more than 800 million tokens, the scanned collection more than 1 billion. All documents are available as UTF-8 encoded XML files with extensive metadata in Dublin Core standard. The text itself is divided into pages which are divided into paragraphs. Every document, page and paragraph has a unique URN which resolves to a web page. Every page element in the XML files is connected to a facsimile image of that page in PDF or JPEG format. We created a viewer in which both versions can be inspected simultaneously. The corpus is available for download in several formats. The corpus can be used for corpus-linguistic and political science research, and is suitable for performing scalability tests for XML information systems.