This paper reports the present results of a research on unsupervised Persian morpheme discovery. In this paper we present a method for discovering the morphemes of Persian language through automatic analysis of corpora. We utilized a Minimum Description Length (MDL) based algorithm with some improvements and applied it to Persian corpus. Our improvements include enhancing the cost function using some heuristics, preventing the split of high frequency chunks, exploiting penalty for first and last letters and distinguishing pre-parts and post-parts. Our improved approach has raised the precision, recall and f-measure of discovery by respectively %32, %17 and %23.
Nowadays, dialogue systems are used in many fields of industry and research. There are successful instances of these systems, such as Apple Siri, Google Assistant, and IBM Watson. Task-oriented dialogue system is a category of these, that are used in specific tasks. They can perform tasks such as booking plane tickets or making restaurant reservations. Shopping is one of the most popular areas on these systems. The bot replaces the human salesperson and interacts with the customers by speaking. To train the models behind the scenes of these systems, annotated data is needed. In this paper, we developed a dataset of dialogues in the Persian language through crowd-sourcing. We annotated these dialogues to train a model. This dataset contains nearly 22k utterances in 15 different domains and 1061 dialogues. This is the largest Persian dataset in this field, which is provided freely so that future researchers can use it. Also, we proposed some baseline models for natural language understanding (NLU) tasks. These models perform two tasks for NLU: intent classification and entity extraction. The F-1 score metric obtained for intent classification is around 91% and for entity extraction is around 93%, which can be a baseline for future research.
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