Mayank Kejriwal
ABSTRACT
Word embeddings have made enormous inroads in recent years in a wide variety of text mining applications. In this paper, we explore a word embedding-based architecture for predicting the relevance of a role between two financial entities within the context of natural language sentences. In this extended abstract, we propose a pooled approach that uses a collection of sentences to train word embeddings using the skip-gram word2vec architecture. We use the word embeddings to obtain context vectors that are assigned one or more labels based on manual annotations. We train a machine learning classifier using the labeled context vectors, and use the trained classifier to predict contextual role relevance on test data. Our approach serves as a good minimal-expertise baseline for the task as it is simple and intuitive, uses open-source modules, requires little feature crafting effort and performs well across roles.
- T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111--3119, 2013. Google Scholar
Digital Library
- L. Raschid, D. Burdick, M. Flood, J. Grant, J. Langsam, I. Soboroff, and E. Zotkina. Financial entity identification and information integration (FEIII) challenge 2017: The report of the organizing committee. In Proceedings of the Workshop on Data Science for Macro-Modeling (DSMM@SIGMOD), 2017. Google ScholarDigital Library
- C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 334--342. ACM, 2001. Google ScholarDigital Library
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