Extracting Entities and Relations in Analyst Stock Ratings News

Abstract

Massive volumes of finance-related data are created on the Internet daily,whether on question-answering forums, news articles, or stocks analysis sites. This data can be critical in the decision-making process fortargeting investments in the stock market. Our research paper aimsto extract information from such sources in order to utilize the volumes of data, which is impossible to process manually. In particular,analysts’ ratings on the stocks of well-known companies are considereddata of interest. Two subdomains of Information Extraction will be performed on the analysts’ ratings, Named Entity Recognition and RelationExtraction. The former is a technique for extracting entities from araw text, giving us insights into phrases that have a special meaning inthe domain of interest. However, apart from the actual positions andlabels of those phrases, it lacks the ability to explain the mutual relations between them, bringing up the necessity of the latter model, whichexplains the semantic relationships between entities and enriches theamount of information we can extract when stacked on top of the NamedEntity Recognition model. This study is based on the employment of different models for word embedding and different Deep Learning classification architectures for extracting the entities and predicting relationsbetween them. Furthermore, the multilingual abilities of a joint pipelineare being explored by combining English and German corpora. For bothsubtasks, we record state-of-the-art performances of 97.69% F1 score fornamed entity recognition and 89.70% F1 score for relation extraction.

Legal Entity Extraction: An Experimental Study of NER Approach for Legal Documents

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