Abstract
The semantic relation detection among entities from unstructured text is an important task in automatic knowledge construction to discover new knowledge. Word embeddings have been successful in capturing semantic relations among entities in unstructured text. In this work we propose to use WordNet as a knowledge base to extract semantic relations among entities and measure how well word embeddings vectors capture semantic regularities by themselves, using state-of-art classification model to detect semantic relations. We present semantic relation capture f-measure score in word embedding vectors of 94.9%, the semantic relations addressed in this work are taxonomic relations (hypernym-hyponym) and part-of relations (holonym-meronym).
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This research was supported/partially supported by MyDCI (Maestría y Doctorado en Ciencias e Ingeniería).
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Navarro-Almanza, R., Licea, G., Juárez-Ramírez, R., Mendoza, O. (2017). Semantic Capture Analysis in Word Embedding Vectors Using Convolutional Neural Network. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-56535-4_11
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