Dictionary-based effective and efficient Turkish lemmatizer

In this thesis, we present a new Turkish lemmatizer that runs on the GPU and investigate its accuracy and performance. Turkish is an agglutinative language, with a rich morphological structure, contains homographic and inflectional word forms which are lowering the accuracy of stemmers. Thus, in Turkish information retrieval systems, the ability to lemmatize Turkish words efficiently and effectively is important. Our study aims at developing a fast dictionary based lemmatizing approach for indexing and searching documents in Turkish.Recent introduction of CUDA (Compute Unified Device Architecture) libraries for high performance computing on graphic processing units (GPUs) by NVIDIA has increased the trend to use GPUs as general purpose performance environment (GPGPU). Today researchers started to exploit GPU?s high computational capability through CUDA in many applicative contexts requiring intensive use of computational resources such as molecular dynamics, fluid dynamics, cryptology, computer vision, astrophysics and genetics.(e.g. Manavski and Valle, 2008 ) CUDA can be used also in the information retrieval because of its massively workload. Our program, achieves a speedup of as much as 90 times on a recent GPU (NVIDIA GeForce GT240M) over the equivalent CPU-bound version, ultimately with the use of parallelized execution of lemmatization algorithm using a data structure inspired from ?Radix Trie?. Here, we present evaluation results of our string lemmatizing kernels for use in CUDA, which executes parallelized lemmatizing for a test set of query strings. We compared our lemmatization algorithm running on GPU with the serial CPU bound version, and explored issues associated with efficient use of GPU resources with eight different algorithms.