IITK在FinSim任务：通过上下文无关和上下文化词嵌入在金融领域中的上位词检测
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings
摘要：
在本文中，我们提出了我们的方法FinSim 2020共享任务“学习金融领域的语义表示”。该任务的目标是将金融术语分类到外部本体中最相关的上位（或顶级）概念中。在我们的分析中，我们利用上下文相关和上下文无关的词嵌入。我们的系统部署了在语料库（英文版的财务招股说明书）上从头开始训练的Word2vec嵌入以及预训练的BERT嵌入。我们根据领域规则将测试数据集划分为两个子集。对于一个子集，我们使用无监督的距离测量来分类术语。对于第二个子集，我们使用简单的监督分类器，如朴素贝叶斯，在嵌入的顶部，以达到最终的预测。最后，我们将两者的结果结合起来。我们的系统根据这两个指标排名第一，即，平均等级和准确度。
英文摘要：
In this paper, we present our approaches for the FinSim 2020 shared task on "Learning Semantic Representations for the Financial Domain". The goal of this task is to classify financial terms into the most relevant hypernym (or top-level) concept in an external ontology. We leverage both context-dependent and context-independent word embeddings in our analysis. Our systems deploy Word2vec embeddings trained from scratch on the corpus (Financial Prospectus in English) along with pre-trained BERT embeddings. We divide the test dataset into two subsets based on a domain rule. For one subset, we use unsupervised distance measures to classify the term. For the second subset, we use simple supervised classifiers like Naive Bayes, on top of the embeddings, to arrive at a final prediction. Finally, we combine both the results. Our system ranks 1st based on both the metrics, i.e., mean rank and accuracy.
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。
