This repository is basically a Bi-LSTM based sequence tagger in both Tensorflow and Dynet which can utilize several sources of information about each word unit like word embeddings, character based embeddings and morphological tags from an FST to obtain the representation for that specific word unit. The details of the methods is explained in (Gungor et al., 2017).
Gungor, O., Yildiz, E., Uskudarli, S., & Gungor, T. (2017). Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages. arXiv preprint arXiv:1706.00506 .
This tool disambiguates the potential morphological analyses of a Turkish word given its context. It employs word representations that are composed of character based embeddings which are based on the surface form and word embeddings. It models each potential morphological analysis as a composition of two embeddings: embeddings based on character sequences of its root and embeddings based on morpheme sequence in the analysis. This is a re-implementation of (Shen et al., 2016).
Gungor, O., Yildiz, E., Uskudarli, S., & Gungor, T. (2017). Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages. arXiv preprint arXiv:1706.00506 .
Shen, Q., Clothiaux, D., Tagtow, E., Littell, P., & Dyer, C. (2016). The Role of Context in Neural Morphological Disambiguation. In COLING (pp. 181-191).