F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification

The share prices of listed companies in the stock trading market are prone to be influenced by various events. Performing event detection could help people to timely identify investment risks and opportunities accompanying these events. The financial events inherently present hierarchical structures, which could be represented as tree-structured schemes in real-life applications, and detecting events could be modeled as a hierarchical multilabel text classification problem, where an event is designated to a tree node with a sequence of hierarchical event category labels. Conventional hierarchical multi-label text classification methods usually ignore the hierarchical relationships existing in the event classification scheme, and treat the hierarchical labels associated with an event as uniform labels, where correct or wrong label predictions are assigned with equal rewards or penalties. In this paper, we propose a neural hierarchical multilabel text classification method, namely F-HMTC, for a financial application scenario with massive event category labels. F-HMTC learns the latent features based on bidirectional encoder representations from transformers, and directly maps them to hierarchical labels with a delicate hierarchybased loss layer. We conduct extensive experiments on a private financial dataset with elaboratelyannotated labels, and F-HMTC consistently outperforms state-of-art baselines by substantial margins. We will release both the source codes and dataset on a public repository 1.

[1]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[2]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[3]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[4]  Michalis Vazirgiannis,et al.  Text Categorization as a Graph Classification Problem , 2015, ACL.

[5]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[6]  Tomas Mikolov,et al.  Bag of Tricks for Efficient Text Classification , 2016, EACL.

[7]  Gary Geunbae Lee,et al.  Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2012, ACL 2012.

[8]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[9]  Yann LeCun,et al.  Very Deep Convolutional Networks for Text Classification , 2016, EACL.

[10]  Charu C. Aggarwal,et al.  A Survey of Text Classification Algorithms , 2012, Mining Text Data.

[11]  Xuanjing Huang,et al.  Recurrent Neural Network for Text Classification with Multi-Task Learning , 2016, IJCAI.

[12]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[13]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[14]  Celine Vens,et al.  Labelling strategies for hierarchical multi-label classification techniques , 2016, Pattern Recognit..

[15]  Chen Ping.  Hierarchical text classification and evaluation , 2010 .

[16]  Stan Matwin,et al.  Functional Annotation of Genes Using Hierarchical Text Categorization , 2005 .

[17]  Zheng Zhang,et al.  Star-Transformer , 2019, NAACL.

[18]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[19]  Yiming Yang,et al.  Recursive regularization for large-scale classification with hierarchical and graphical dependencies , 2013, KDD.

[20]  Jianxin Li,et al.  Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN , 2018, WWW.

[21]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.