Quo Vadis, Math Information Retrieval
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2019 |
| MU Faculty or unit | |
| Citation | |
| Web | |
| Keywords | math information retrieval; question answering; STEM; digital mathematical libraries; embeddings; MIaS; MIaSNG; DML |
| Description | With the exponential growth of information in the digital form, information retrieval and querying digital libraries is of paramount importance, and mathematical and technical STEM documents are not an exception. The key for precise searching is the adequate and unambiguous representation of documents, paragraphs, sentences and words, which we are going to evaluate. We are presenting a roadmap to tackle the problem of searching and question answering in the digital mathematical libraries, and discuss the pros and cons of promising approaches primarily for the key part, namely the document representation: several types of embeddings, topic mixtures and LSTM. The listed representation learning options will be evaluated at the next ARQMath evaluation lab of CLEF 2020 conference. |
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