The goal of this master thesis is to design and implement a named entity recognition and linking algorithm. A part of this goal is to propose and create a knowledge base that will be used in the algorithm. Because of the limited amount of data for languages other than English, we want to be able to train our method on one language, and then transfer the learned parameters to other languages (that do not have enough training data). The thesis consists of description of available knowledge bases, existing methods and design and implementation of our own knowledge base and entity linking method. Our method achieves state of the art result on a few variants of the AIDA CoNLL-YAGO dataset. The method also obtains comparable results on a sample of Czech annotated data from the PDT dataset using the parameters trained on the English CoNLL dataset. Powered by TCPDF (www.tcpdf.org)

This thesis explores approaches for extracting company mentions from financial newsarticles that carry a central role in the news. The thesis introduces the task of salient named entity extraction (SNEE): extract all salient named entity mentions in a text document. Moreover, a neural sequence labeling approach is explored to address the SNEE task in an end-to-end fashion, both using a single-task and a multi-task learning setup. In order to train the models, a new procedure for automatically creating SNEE annotations for an existing news article corpus is explored. The neural sequence labeling approaches are compared against a two-stage approach utilizing NLP parsers, a knowledge base and a salience classifier. Textual features inspired from related work in salient entity detection are evaluated to determine what combination of features results in the highest performance on the SNEE task when used by a salience classifier. The experiments show that the difference in performance between the two-stage approach and the best performing sequence labeling approach is marginal, demonstrating the potential of the end-to-end sequence labeling approach on the SNEE task.