Joint Learning of Named Entity Recognition and Dependency Parsing using Separate Datasets

Arda Akdemir, Tunga Güngör


Joint learning of different NLP-related tasks is an emerging research eld in Machine Learning. Yet, most of the recent models proposed on joint learning require a dataset that is annotated jointly for all the tasks involved. Such datasets are available only for frequently used languages. In this paper, we propose a novel BiLSTM CRF based joint learning model for dependency parsing and named entity recognition tasks, which has not been employed before for Turkish to the best of our knowledge. This enables joint learning of various tasks for languages that have limited amount of annotated datasets. Our model, tested on a frequently used NER dataset for Turkish, has comparable results with the state-of-the-art systems. We also show that our proposed model out performs the joint learning model which uses a single dataset.


Joint learning, named entity recognition, dependency parsing, turkish.

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