Martin Reynaert

• PhD
• Senior Researcher at Tilburg University

We address the problem of creating and evaluating quality Neo-Latin word embeddings for the purpose of philosophical research, adapting the Nonce2Vec tool to learn embeddings from Neo-Latin sentences. This distributional semantic modeling tool can learn from tiny data incrementally, using a larger background corpus for initialization. We conduct tw...

"But I don’t know how to work with [name of tool or resource]" is something one often hears when researchers in Human and Social Sciences (HSS) are confronted with language technology, be it written or spoken, tools or resources. The TTNWW project shows that these researchers do not need to be experts in language or speech technology, or to know al...

CLARIN activities in the Netherlands in 2015 are in transition between the first national project CLARIN-NL and its successor CLARIAH. In this paper we give an overview of important infrastructure developments which have taken place throughout the first and which are taken to a further level in the second. We show how relatively small accomplishmen...

We give an overview of activities undertaken in the sidelines of our automatic OCR post-correction core business over the past few years. We present ongoing projects in the Netherlands in which Text-Induced Corpus Clean-up plays a part. We describe the infrastructure we are building to help improve the overall text quality of large digitized text c...

In this paper we present FoLiA, a Format for Linguistic Annotation, and conduct a comparative study with other annotation schemes, including the Linguistic Annotation Framework (LAF), the Text Encoding Initiative (TEI) and Text Corpus Format (TCF). An additional point of focus is the interoperability between FoLiA and metadata standards such as the...

The construction of a large and richly annotated corpus of written Dutch was identified as one of the priorities of the STEVIN programme. Such a corpus, sampling texts from conventional and new media, is invaluable for scientific research and application development. The present chapter describes how in two consecutive STEVIN-funded projects, viz....

This article is about the type of language that is offered to learners in textbooks, using the example of Russian. Many modern textbooks of Russian as a foreign language aim at efficient development of oral communication skills. However, some expressions used in the textbooks are not typical for everyday language. We claim that textbooks’ content s...

We present a new approach based on anagram hashing to handle globally the lexical variation in large and noisy text collections. Lexical variation addressed by spelling correction systems is primarily typographical variation. This is typically handled in a local fashion: given one particular text string some system of retrieving near-neighbors is a...

In The Low Countries, a major reference corpus for written Dutch is beingbuilt. We discuss the interplay between data acquisition and data processingduring the creation of the SoNaR Corpus. Based on developments in traditionalcorpus compiling and new web harvesting approaches, SoNaR is designed tocontain 500 million words, balanced over 36 text typ...

We present a new approach based on anagram hashing to globally handle the typographical variation in large and possibly noisy text collections. Typographical variation is typically handled in a local fashion: given one particular text string some system of retrieving near-neighbours is applied, where near-neighbours are other text strings that diff...

Some time in the future, some spelling error correction system will correct all the errors, and only the errors. We need evaluation metrics that will tell us when this has been achieved and that can help guide us there. We survey the current practice in the form of the evaluation scheme of the latest major publication on spelling correcti on in a l...

The computational linguistics community in The Netherlands and Belgium has long recognized the dire need for a major reference corpus of written Dutch. In part to answer this need, the STEVIN programme was established. To pave the way for the effective building of a 500-million-word reference corpus of written Dutch, a pilot project was established...

This paper proposes a non-interactive system for reducing the level of OCR-induced typographical variation in large text collections, contemporary and historical. Text-Induced Corpus Clean-up or ticcl (pronounce 'tickle') focuses on high-frequency words derived from the corpus to be cleaned and gathers all typographical variants for any particular...

We explore the feasibility of using only unsupervised means to identify non-words, i.e. typos, in a frequency list derived from a large corpus of Dutch and to distinguish between these non-words and real-words in the language. We call the system we built and evaluate in this paper CICCL, which stands for 'Corpus-Induced Corpus Clean-up'. The algori...

We present TISC, a language-independent and context-sensitive spelling checking and correction system designed to facilitate the automatic removal of non-word spelling errors in large corpora. Its lexicon is derived from a very large corpus of raw text, without supervision, and contains word unigrams and word bigrams. It is stored in a novel repres...

We present TISC, a multilingual, language-independent and context-sensitive spelling checking and correction system designed to facilitate the automatic removal of non-word spelling errors in large corpora. Its lexicon is derived from raw text corpora, without supervision, and contains word unigrams and word bigrams. The system employs input contex...

We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shallow, easy-to-compute features. We train the algorithms on both tasks individually and on the two tasks simultaneously. The parameters of both algorithms and the selection of features are opti...

We describe results on pitch accent placement in Dutch text obtained with a memory-based learning approach. The training material consists of newspaper texts that have been prosodically annotated by humans, and subsequently enriched with linguistic features and informational metrics using generally available, lowcost, shallow, knowledge-poor tools....

In this paper we report on the experiences gained in the recent construction of the SoNaR corpus, a 500 MW reference corpus of contemporary, written Dutch. It shows what can realistically be done within the confines of a project setting where there are limitations to the duration in time as well to the budget, employing current state-of-the-art too...

Thesis (M.A.)--University of Wales (U.C.N.W., Bangor: Linguistics), 1988.