Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration - NEWS '09

Numerals in Financial Narratives

From Opinion Mining to Financial Argument Mining - SpringerBriefs in Computer Science ◽

10.1007/978-981-16-2881-8_5 ◽

2021 ◽

pp. 55-71

Author(s):

Chung-Chi Chen ◽

Hen-Hsen Huang ◽

Hsin-Hsi Chen

Keyword(s):

Experimental Results ◽

Future Research ◽

Shared Task ◽

Named Entities ◽

Research Directions ◽

Future Research Directions ◽

Financial Narratives

AbstractNumerals are more common in financial narratives than in documents from other domains, which makes understanding numerals very important when analyzing financial documents. In this chapter, we summarize our work on numerals in financial narratives and share findings from the FinNum shared task series in the 14th and 15th NTCIR Conferences. In Sect. 5.1, we discuss how to understand the meaning of a given numeral, and in Sect. 5.2, we discuss numeral attachment, where we link numerals and named entities. In Sect. 5.3, we show experimental results from downstream tasks that demonstrate the importance of numeral understanding in financial narratives. We conclude by proposing future research directions in Sect. 5.4.

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R-BERT FOR RELATIONSHIP EXTRACTION ON RUSSIAN BUSINESS DOCUMENTS

Computational Linguistics and Intellectual Technologies ◽

10.28995/2075-7182-2020-19-467-473 ◽

2020 ◽

Author(s):

V. A. Korzun ◽

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Named Entity Recognition ◽

Relation Extraction ◽

Entity Recognition ◽

Shared Task ◽

Named Entities ◽

Named Entity ◽

Relationship Extraction

This paper provides results of participation in the Russian Relation Extraction for Business shared task (RuREBus) within DialogueEvaluation 2020. Our team took the first place among 5 other teams in Relation Extraction with Named Entities task. The experiments showed that the best model is based on R-BERT model. R-BERT achieved significant result in comparison with models based on Convolutional or Recurrent Neural Networks on the SemEval-2010 task 8 relational dataset. In order to adapt this model to RuREBus task we also added some modifications like negative sampling. In addition, we have tested other models for Relation Extraction and Named Entity Recognition tasks.

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VLSP Shared Task: Named Entity Recognition

Journal of Computer Science and Cybernetics ◽

10.15625/1813-9663/34/4/13161 ◽

2019 ◽

Vol 34 (4) ◽

pp. 283-294 ◽

Cited By ~ 1

Author(s):

Huyen T M Nguyen ◽

Quyen T Ngo ◽

Luong X Vu ◽

Vu M Tran ◽

Hien T T Nguyen

Keyword(s):

Named Entity Recognition ◽

Objective Evaluation ◽

Entity Recognition ◽

Research Projects ◽

Shared Task ◽

High Quality ◽

Named Entities ◽

Named Entity ◽

The World ◽

Syntactic Annotation

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.

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