Sentence and Word Embedding Employed in Open Question-Answering
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) |
| MU Faculty or unit | |
| Citation | |
| Field | Linguistics |
| Keywords | question answering; word embedding; word2vec; AQA; Simple Question Answering Database; SQAD |
| Description | The Automatic Question Answering, or AQA, system is a representative of open domain QA systems, where the answer selection process leans on syntactic and semantic similarities between the question and the answering text snippets. Such approach is specifically oriented to languages with fine grained syntactic and morphologic features that help to guide the correct QA match. In this paper, we present the latest results of the AQA system with new word embedding criteria implementation. All AQA processing steps (question processing, answer selection and answer extraction) are syntax-based with advanced scoring obtained by a combination of several similarity criteria (TF-IDF, tree distance, ...). Adding the word embedding parameters helped to resolve the QA match in cases, where the answer is expressed by semantically near equivalents. We describe the design and implementation of the whole QA process and provide a new evaluation of the AQA system with the word embedding criteria measured with an expanded version of Simple Question-Answering Database, or SQAD, with more than 3000 question-answer pairs extracted from the Czech Wikipedia. |
| Related projects: |