VLSP Shared Task: Named Entity Recognition

Named entities (NE) are phrases that contain the names of persons, organizations, locations, times and quantities, monetary values, percentages, etc. Named Entity Recognition (NER) is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction, which has attracted researchers all over the world since 1990s. For Vietnamese language, although there exists some research projects and publications on NER task before 2016, no systematic comparison of the performance of NER systems has been done. In 2016, the organizing committee of the VLSP workshop decided to launch the first NER shared task, in order to get an objective evaluation of Vietnamese NER systems and to promote the development of high quality systems. As a result, the first dataset with morpho-syntactic and NE annotations has been released for benchmarking NER systems. At VLSP 2018, the NER shared task has been organized for the second time, providing a bigger dataset containing texts from various domains, but without morpho-syntactic annotation. These resources are available for research purpose via the VLSP website vlsp.org.vn/resources. In this paper, we describe the datasets as well as the evaluation results obtained from these two campaigns

Improved Named Entity Recognition Through SVM-Based Combination

Named Entity Extraction (NER) consists in identifying specific textual expressions, which represent various types of concepts: persons, locations, organizations, etc. It is an important part of natural language processing, because it is often used when building more advanced text-based tools, especially in the context of information extraction. Consequently, many NER tools are now available, designed to handle various sorts of texts, languages and entity types. A recent study on biographical texts showed the overall indices used to assess the performance of these tools hide the fact they can behave rather differently depending on the textual context, and could actually be complementary. In this work, we check this assumption by proposing two methods allowing to combine several NER tools: one relies on a voting process and the other is SVM-based. Both take advantage of a global text feature to guide the combination process. We extend an existing corpus to provide enough data for training and testing. We implement an open source flexible platform aiming at benchmarking NER tools. We apply our combination methods on a selection of NER tools, including state-of-the-art ones, as well as our custom tool specifically designed to process hyperlinked biographical texts. Our results show both proposed combination approaches outmatch the individual performance of all the considered standalone NER tools. Of the two, the SVM-based approach reaches the highest performance

Automatic rule learning exploiting morphological features for named entity recognition in Turkish

Named entity recognition (NER) is one of the basic tasks in automatic extraction of information from natural language texts. In this paper, we describe an automatic rule learning method that exploits different features of the input text to identify the named entities located in the natural language texts. Moreover, we explore the use of morphological features for extracting named entities from Turkish texts. We believe that the developed system can also be used for other agglutinative languages. The paper also provides a comprehensive overview of the field by reviewing the NER research literature. We conducted our experiments on the TurkIE dataset, a corpus of articles collected from different Turkish newspapers. Our method achieved an average F-score of 91.08% on the dataset. The results of the comparative experiments demonstrate that the developed technique is successfully applicable to the task of automatic NER and exploiting morphological features can significantly improve the NER from Turkish, an agglutinative language. © The Author(s) 2011