This paper proposes a post-editing model in which our three-level rule-based automatic post-editing engine called Grafix is presented to refine the output of machine translation systems. The type of corrections on sentences varies from lexical transformation to complex syntactical rearrangement. The experimental results both in manual and automatic evaluations show that the proposed system is a...

We present the Uppsala Persian Dependency Treebank (UPDT) with a syntactic annotation scheme based on Stanford Typed Dependencies. The treebank consists of 6,000 sentences and 151,671 tokens with an average sentence length of 25 words. The data is from different genres, including newspaper articles and fiction, as well as technical descriptions and texts about culture and art, taken from the op...

In this paper, an attempt to develop a phrase-based statistical machine translation between English and Persian languages (PersianSMT) is described. Creation of the largest English-Persian parallel corpus yet presented by the use of movie subtitles is a part of this work. Two major goals are followed here: the first one is to show the main problems observed in the output of the PersianSMT syste...

This paper presents a novel method to extract the collocations of the Persian language using a parallel corpus. The method is applicable having a parallel corpus between a target language and any other high-resource one. Without the need for an accurate parser for the target side, it aims to parse the sentences to capture long distance collocations and to generate more precise results. A traini...

This paper introduces, motivates, and illustrates a new approach to the model-ing of lexicon base. This lexicon is used in the process of scene generation, described in terms of sentences in Persian. The lexicon base is rich enough to handle scene expressions. It contains not only case frame information, but also it helps to determine the following (a) meaning of spatial propositions; (b) the p...

This report explains our Persian plagiarism detection system which we used to submit our run to Persian PlagDet competition at FIRE 2016. The system was constructed through four main stages. First is pre-processing and tokenization. Second is constructing a corpus of sentences from combination of source and suspicious document pair. Each sentence considered to be a document and represented as a...

Unsupervised dependency parsing is one of the most challenging tasks in natural languages processing. The task involves finding the best possible dependency trees from raw sentences without getting any aid from annotated data. In this paper, we illustrate that by applying a supervised incremental parsing model to unsupervised parsing; parsing with a linear time complexity will be faster than th...

Unsupervised dependency parsing is one of the most challenging tasks in natural languages processing. The task involves finding the best possible dependency trees from raw sentences without getting any aid from annotated data. In this paper, we illustrate that by applying a supervised incremental parsing model to unsupervised parsing; parsing with a linear time complexity will be faster than th...