Using Word Embeddings in Detection of Temporal Expressions in Turkish Texts - Emirali, Ensar; Karsligil, M. Elif (IEEE, 2022)
Developing systems for automatically detection of date, time, duration and set expressions containing time information in texts is within the scope of Natural Language Processing research field. When studies for Turkish in the literature are reviewed, it is observed that only date and time expressions are included in the expressions detected by the models developed within the scope of Named Entity Recognition. There are studies to develop only rule-based systems on the subject of detection of temporal expressions in Turkish. Within the scope of this study, first Artificial Neural Networks based model for the detection of temporal expressions in Turkish texts is developed. The input of the developed model is word embeddings. In this study, the developed model success with using word embeddings built by different methods is measured on a dataset consisting of Turkish complaint texts collected from internet websites. By comparing the success of word embeddings on the detection of temporal expressions with the coverage percentages of word embeddings on the dataset, it is concluded that there is no correlation between them.
Keywords: temporal expressions; word embeddings; biLSTM; NAMED ENTITY RECOGNITION; Computer Science; Engineering; Telecommunications