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DICoE@FinSim-3：使用增强术语和基于距离的特征进行金融上位词检测

DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features

Institute of Informatics and Telecommunications（信息与电信研究所）；Athens University of Economics and Business（雅典经济与商业大学）；National Centre for Scientific Research “Demokritos”（“德谟克利特”国家科学研究中心）

Lefteris Loukas, Konstantinos Bougiatiotis, Manos Fergadiotis, Dimitris Mavroeidis, Elias Zavitsanos

备注：
6 pages, Proceedings of the Third Workshop on Financial Technology and Natural Language Processing (FinNLP@IJCAI-2021)
发表信息：
In Proceedings of the Third Workshop on Financial Technology and Natural Language Processing (FinNLP 2021)
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AI总结：

本文提出DICoE团队在FinSim-3金融上位词检测任务中的方法，通过Investopedia定义增强术语，结合金融词嵌入、Logistic Regression分类器、手工与距离特征及词汇表外术语替换策略，最终在排行榜上获得第4名。

摘要：

我们介绍DICoE团队参加FinSim-3的提交成果，即第三届金融领域语义相似度学习共享任务。该任务提供一组金融领域术语，要求将其分类到金融本体中最相关的上位词。在利用Investopedia定义对术语进行增强后，我们的系统在金融词嵌入上采用Logistic Regression分类器，并结合手工构造特征与基于距离的特征。此外，我们在该任务中首次对词汇表外术语采用不同的替换方法，从而提升了性能。最后，我们还对由各种金融语料库生成的词表示进行了实验。我们表现最佳的提交在任务排行榜上排名第4。

英文摘要：

We present the submission of team DICoE for FinSim-3, the 3rd Shared Task on Learning Semantic Similarities for the Financial Domain. The task provides a set of terms in the financial domain and requires to classify them into the most relevant hypernym from a financial ontology. After augmenting the terms with their Investopedia definitions, our system employs a Logistic Regression classifier over financial word embeddings and a mix of hand-crafted and distance-based features. Also, for the first time in this task, we employ different replacement methods for out-of-vocabulary terms, leading to improved performance. Finally, we have also experimented with word representations generated from various financial corpora. Our best-performing submission ranked 4th on the task's leaderboard.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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