A classification-based approach to economic event detection in Dutch news text

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text

Economic event detection in company-specific news text

This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups

SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)

This paper describes the second edition of the shared task on Taxonomy Extraction Evaluation organised as part of SemEval 2016. This task aims to extract hypernym-hyponym relations between a given list of domain-specific terms and then to construct a domain taxonomy based on them. TExEval-2 introduced a multilingual setting for this task, covering four different languages including English, Dutch, Italian and French from domains as diverse as environment, food and science. A total of 62 runs submitted by 5 different teams were evaluated using structural measures, by comparison with gold standard taxonomies and by manual quality assessment of novel relations.Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (INSIGHT

Dutch hypernym detection : does decompounding help?

This research presents experiments carried out to improve the precision and recall of Dutch hypernym detection. To do so, we applied a data-driven semantic relation finder that starts from a list of automatically extracted domain-specific terms from technical corpora, and generates a list of hypernym relations between these terms. As Dutch technical terms often consist of compounds written in one orthographic unit, we investigated the impact of a decompounding module on the performance of the hypernym detection system. In addition, we also improved the precision of the system by designing filters taking into account statistical and linguistic information. The experimental results show that both the precision and recall of the hypernym detection system improved, and that the decompounding module is especially effective for hypernym detection in Dutch

Discovering missing Wikipedia inter-language links by means of cross-lingual word sense disambiguation

Wikipedia is a very popular online multilingual encyclopedia that contains millions of articles covering most written languages. Wikipedia pages contain monolingual hypertext links to other pages, as well as inter-language links to the corresponding pages in other languages. These inter-language links, however, are not always complete. We present a prototype for a cross-lingual link discovery tool that discovers missing Wikipedia inter-language links to corresponding pages in other languages for ambiguous nouns. Although the framework of our approach is language-independent, we built a prototype for our application using Dutch as an input language and Spanish, Italian, English, French and German as target languages. The input for our system is a set of Dutch pages for a given ambiguous noun, and the output of the system is a set of links to the corresponding pages in our five target languages. Our link discovery application contains two submodules. In a first step all pages are retrieved that contain a translation (in our five target languages) of the ambiguous word in the page title (Greedy crawler module), whereas in a second step all corresponding pages are linked between the focus language (being Dutch in our case) and the five target languages (Cross-lingual web page linker module). We consider this second step as a disambiguation task and apply a cross-lingual Word Sense Disambiguation framework to determine whether two pages refer to the same content or not