Modeling Persian Verb Morphology to Improve English-Persian Machine Translation

Mahmoudi, Alireza; Faili, Heshaam; Arabsorkhi, Mohsen

doi:10.1007/978-3-642-45114-0_33

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to be noticed that the spell checker algorithms concentrate on the spelling errors which are often caused by operational and cognitive mistakes [1], thus the errors occurring due to the usage of space and half-space in a wrong manner are usually ignored by spell checker algorithms. Few researchers have worked on editing the spacing in Persian words [2][3][4]. A toolkit is presented by Shamsfard et al [2] to detect boundaries of words, phrases and sentences, check and correct the spelling, do morphological analysis and Part-Of-Speech (POS) tagging.…”

Section: ‫حاصل‬ ‫ضرب‬mentioning

confidence: 99%

See 1 more Smart Citation

A new model for persian multi-part words edition based on statistical machine translation

Zahedi

Arjomandzadeh

2016

JAIDM

View full text Add to dashboard Cite

Multi-part words in English language are hyphenated and hyphen is used to separate different parts. Persian language consists of multi-part words as well. Based on Persian morphology, half-space character is needed to separate parts of multi-part words where in many cases people incorrectly use space character instead of half-space character. This common incorrectly use of space leads to some serious issues in Persian text processing and text readability. In order to cope with the issues, this work proposes a new model to correct spacing in multi-part words. The proposed method is based on statistical machine translation paradigm. In machine translation paradigm, text in source language is translated into a text in destination language on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The proposed method uses statistical machine translation techniques considering unedited multi-part words as a source language and the space-edited multi-part words as a destination language. The results show that the proposed method can edit and improve spacing correction process of Persian multi-part words with a statistically significant accuracy rate.

show abstract

Section: ‫حاصل‬ ‫ضرب‬mentioning

confidence: 99%

“…The approach finds the stems and affixes of words with Finite State Automaton (FSA) and tags them with the part of speech tags. Mahmoudi et al [3] focused only on modeling Persian verb morphology.…”

Section: ‫حاصل‬ ‫ضرب‬mentioning

confidence: 99%

A new model for persian multi-part words edition based on statistical machine translation

Zahedi

Arjomandzadeh

2016

JAIDM

View full text Add to dashboard Cite

show abstract

Using decision tree to hybrid morphology generation of Persian verb for English–Persian translation

Mahmoudi

Faili

2015

Computer Speech & Language

View full text Add to dashboard Cite

Comparing k-means clusters on parallel Persian-English corpus

Khazaei

Ghasemzadeh

2015

JAIDM

View full text Add to dashboard Cite

This paper compares clusters of aligned Persian and English texts obtained from k-means method. Text clustering has many applications in various fields of natural language processing. So far, much English documents clustering research has been accomplished. Now this question arises, are the results of them extendable to other languages? Since the goal of document clustering is grouping of documents based on their content, it is expected that the answer to this question is yes. On the other hand, many differences between various languages can cause the answer to this question to be no. This research has focused on kmeans that is one of the basic and popular document clustering methods. We want to know whether the clusters of aligned Persian and English texts obtained by the k-means are similar. To find an answer to this question, Mizan English-Persian Parallel Corpus was considered as benchmark. After features extraction using text mining techniques and applying the PCA dimension reduction method, the k-means clustering was performed. The morphological difference between English and Persian languages caused the larger feature vector length for Persian. So almost in all experiments, the English results were slightly richer than those in Persian. Aside from these differences, the overall behavior of Persian and English clusters was similar. These similar behaviors showed that results of k-means research on English can be expanded to Persian. Finally, there is hope that despite many differences between various languages, clustering methods may be extendable to other languages.

show abstract

Modeling Persian Verb Morphology to Improve English-Persian Machine Translation

Cited by 3 publications

References 12 publications

A new model for persian multi-part words edition based on statistical machine translation

A new model for persian multi-part words edition based on statistical machine translation

Using decision tree to hybrid morphology generation of Persian verb for English–Persian translation

Comparing k-means clusters on parallel Persian-English corpus

Contact Info

Product

Resources

About