The Experts below are selected from a list of 82080 Experts worldwide ranked by ideXlab platform
Moch Arif Bijaksana - One of the best experts on this subject based on the ideXlab platform.
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cross lingual semantic similarity in pieces of al quran verses Translation using word alignment and semantic vector approach
International Seminar on Information and Communication Technologies, 2019Co-Authors: Reza Amelia, Moch Arif BijaksanaAbstract:Al-Quran is a holy book of Muslims, that serves as a guide of life. Therefore, it is very important to understand the meaning of the Qur'an verse. The understanding is not only in the Qur'an in a single Translation language but also in different Translation Languages. This research proposed the solutions to these problems with the analysis and implementation of Cross-Lingual Seman- tic Similarity on the Translation piece of Al-Qur'an verse using Word alignment and Semantic Vector approach. The experimental result shows that the correla- tion value using combination between Word Alignment and Semantic Vector method is 0,62007 while the correlation value using Word Alignment method is 0,63231, and Semantic Vector method is 0,34841. This value was obtained by comparative value in the form of gold standard, namely the semantic similarity on the pair of Translation pieces on the verses of Al-Quran in Indonesian and English based on human assessment manually. The correlation value is quite low due to the Translation of Indonesian Al-Quran verses by using google trans- late feature before the semantic similarity calculation process produces many results that do not match the sentence of origin that is translated. Therefore the semantic value is much different from the gold standard.
Reza Amelia - One of the best experts on this subject based on the ideXlab platform.
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cross lingual semantic similarity in pieces of al quran verses Translation using word alignment and semantic vector approach
International Seminar on Information and Communication Technologies, 2019Co-Authors: Reza Amelia, Moch Arif BijaksanaAbstract:Al-Quran is a holy book of Muslims, that serves as a guide of life. Therefore, it is very important to understand the meaning of the Qur'an verse. The understanding is not only in the Qur'an in a single Translation language but also in different Translation Languages. This research proposed the solutions to these problems with the analysis and implementation of Cross-Lingual Seman- tic Similarity on the Translation piece of Al-Qur'an verse using Word alignment and Semantic Vector approach. The experimental result shows that the correla- tion value using combination between Word Alignment and Semantic Vector method is 0,62007 while the correlation value using Word Alignment method is 0,63231, and Semantic Vector method is 0,34841. This value was obtained by comparative value in the form of gold standard, namely the semantic similarity on the pair of Translation pieces on the verses of Al-Quran in Indonesian and English based on human assessment manually. The correlation value is quite low due to the Translation of Indonesian Al-Quran verses by using google trans- late feature before the semantic similarity calculation process produces many results that do not match the sentence of origin that is translated. Therefore the semantic value is much different from the gold standard.
Mahesh R Sinha - One of the best experts on this subject based on the ideXlab platform.
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Translation divergence in english sanskrit hindi language pairs
Proceedings of the 3rd International Symposium on Sanskrit Computational Linguistics, 2008Co-Authors: Pawan Goyal, Mahesh R SinhaAbstract:The development of a machine Translation system needs that we identify the patterns of divergence between two Languages. Though a number of MT developers have given attention to this problem, it is difficult to derive general strategies which can be used for any language pair. Therefore, further exploration is always needed to identify different sources of Translation divergence in different pairs of Translation Languages. In this paper, we discuss Translation pattern between English-Sanskrit and Hindi-Sanskrit of various constructions to identify the divergence in English-Sanskrit-Hindi language pairs. This will enable us to come up with strategies to handle these situations and coming up with correct Translation. The base has been the classification of Translation divergence presented by Dorr [Dorr, 1994].
Pawan Goyal - One of the best experts on this subject based on the ideXlab platform.
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Translation divergence in english sanskrit hindi language pairs
Proceedings of the 3rd International Symposium on Sanskrit Computational Linguistics, 2008Co-Authors: Pawan Goyal, Mahesh R SinhaAbstract:The development of a machine Translation system needs that we identify the patterns of divergence between two Languages. Though a number of MT developers have given attention to this problem, it is difficult to derive general strategies which can be used for any language pair. Therefore, further exploration is always needed to identify different sources of Translation divergence in different pairs of Translation Languages. In this paper, we discuss Translation pattern between English-Sanskrit and Hindi-Sanskrit of various constructions to identify the divergence in English-Sanskrit-Hindi language pairs. This will enable us to come up with strategies to handle these situations and coming up with correct Translation. The base has been the classification of Translation divergence presented by Dorr [Dorr, 1994].
Sagit Zohar - One of the best experts on this subject based on the ideXlab platform.
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using schema matching to simplify heterogeneous data Translation
Very Large Data Bases, 1998Co-Authors: Tova Milo, Sagit ZoharAbstract:A broad spectrum of data is available on the Web in distinct heterogeneous sources, and stored under different formats. As the number of systems that utilize this heterogeneous data grows, the importance of data Translation and conversion mechanisms increases greatly. In this paper we present a new Translation system, based on schema-matching, aimed at simplifying the intricate task of data conversion. We observe that in many cases the schema of the data in the source system is very similar to that of the target system. In such cases, much of the Translation work can be done automatically, based on the schemas similarity. This saves a lot of effort for the user, limiting the amount of programming needed. We define common schema and data models, in which schemas and data (resp.) from many common models can be represented. Using a rule-based method, the source schema is compared with the target one, and each component in the source schema is matched with a corresponding component in the target schema. Then, based on the matching achieved, data instances of the source schema can be translated to instances of the target schema. We show that our schema-based Translation system allows a convenient specification and customization of data conversions, and can be easily combined with the traditional data-based Translation Languages.
