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Found 15 papers, 2 papers with code
TurkishDelightNLP: A Neural Turkish NLP Toolkit
We introduce a neural Turkish NLP toolkit called TurkishDelightNLP that performs computational linguistic analyses from morphological level to semantic level that involves tasks such as stemming, morphological segmentation, morphological tagging, part-of-speech tagging, dependency parsing, and semantic parsing, as well as high-level NLP tasks such as named entity recognition.
Bilingual Terminology Extraction Using Neural Word Embeddings on Comparable Corpora
Term and glossary management are vital steps of preparation of every language specialist, and they play a very important role at the stage of education of translation professionals.
Self Attended Stack-Pointer Networks for Learning Long Term Dependencies
We propose a novel deep neural architecture for dependency parsing, which is built upon a Transformer Encoder (Vaswani et al. 2017) and a Stack Pointer Network (Ma et al. 2018).
Turkish Universal Conceptual Cognitive Annotation
This is the initial version of the annotated dataset and we are currently extending the dataset.
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing
Graph-based representation is one of the semantic representation approaches to express the semantic structure of a text.
Android Security using NLP Techniques: A Review
In addition to the application code, Android applications have some metadata that could be useful for security analysis of applications.
Characters or Morphemes: How to Represent Words?
The results show that morpheme-based models are better at learning word representations of morphologically complex languages compared to character-based and character n-gram level models since the morphemes help to incorporate more syntactic knowledge in learning, that makes morpheme-based models better at syntactic tasks.
Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation
This article presents a probabilistic hierarchical clustering model for morphological segmentation.
Joint PoS Tagging and Stemming for Agglutinative Languages
Our results show that joint POS tagging and stemming improves PoS tagging scores.
Building Morphological Chains for Agglutinative Languages
In this paper, we build morphological chains for agglutinative languages by using a log-linear model for the morphological segmentation task.
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation
Our results show that using different information sources such as neural word embeddings and letter successor variety as prior information improves morphological segmentation in a Bayesian model.
Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets
We learn inflectional and derivational morpheme tags in Turkish by using conditional random fields (CRF) and we employ the morpheme tags in part-of-speech (PoS) tagging by using hidden Markov models (HMMs) to mitigate sparsity.
