Automatic query expansion for vehicle repair documents through user behavior
The process of Information Retrieval (IR) by query driven search engines have become an essential part of the customer experience in any data related digital product. The accuracy and completeness of the search results is a matter of great interest and a crucial key performance indicator. An importa...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/121326 |
| Acceso en línea: | http://hdl.handle.net/10609/121326 |
| Access Level: | acceso abierto |
| Palabra clave: | nlp embeddings query expansion synonyms pln incorporaciones expansión de consultas sinónimos incorporacions expansió de consultes sinònims Artificial intelligence -- TFM Intel·ligència artificial -- TFM Inteligencia artificial -- TFM |
| Sumario: | The process of Information Retrieval (IR) by query driven search engines have become an essential part of the customer experience in any data related digital product. The accuracy and completeness of the search results is a matter of great interest and a crucial key performance indicator. An important enhancer for search engines is query expansion Query Expansion (QE), where equivalent search queries Equivalent Search Query (ESQ) are added to the original request to increase recall. ESQs can be discovered using the same tools as synonym discovery given certain considerations, taking advantage of the fact that synonym discovery is a well developed field of Natural Language Processing (NLP) with many available techniques. The motivation for this project is to use the tools available in NLP Machine Learning (ML) to automatically detect ESQs. For this a large sample of logs describing search query customer behavior was used. This data set was obtained from a live enterprise product that publishes repair documents for automobiles. Graph embeddings through an implementation method called node2Vec and vector cosine similarity is the chosen discovery method for the ESQs. The conclusion of the experiment is that while usable search expansion queries are discovered, extra human intervention or further automatic selection is necessary to filter the valuable cases from the large number of found cases, even working within a strict similarity threshold. |
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