MATERIALS AND METHODS

The CArabicMT system is based on the transfer-based architecture with three major stages: An analysis stage, a transfer stage and a generation stage. This is done through some of translation process as according to Shirko et al. (2010).

The following summarized the main processes of the Rule based approach in the CArabicMT system:

Fig. 1:	The Architecture of classical Arabic-English MT

The architecture of CArabicMT system is given in Fig. 1.

Transfer-based machine translation: In this section, we will describe in more details the transfer approach and its three major stages: analysis stage, transfer stage and generation stage (Xiang et al., 2011). Moreover, we will show, given examples from classic Arabic, how they process an Arabic sentence and translate it to its English equivalent.

Analysis stage: NLP systems and MT systems require identifying words in texts in order to determine their syntactic and semantic properties (Li et al., 2009).

First of all, a classic Arabic sentence is passed through a tokenization module, in which the sentence is divided into tokens. By token, we mean the smallest syntactic unit; it can be a word, a part of a word (Wang et al., 2008).

Fig. 2:	Architecture of the parser

Table 3:	Example of the morphological analyzer output

In analysis stage the information about source language only are needed.

Secondly, an Arabic morphological analyzer is used to extract stems of the Arabic words and to identify their morphological and syntactic properties such as gender, number, tense, etc.

Table 3 show an example of the morphological analysis output given the following:

Syntactical analysis (Parsing) is the computer process of analyzing a sequence of tokens to determine its grammatical structure with respect to a given formal grammar; the parsing transforms input text into a data structure, usually a tree (Al-Saidat and Al-Momani, 2010). In the parsing, the classical Arabic sentence is represented in a tree of phrases, parse tree, where each phrase. Figure 2 shows the parse tree of an Arabic sentence.

Transfer stage: The transfer stage is a system of rules that relate words and structures in one language to words and structures of another language (Shirko et al., 2010). Transfer starts with the output of the analysis phase and ends where the phase of generation starts (Shquier and Sembok, 2008). In the extent of this research; the translation actually occurs in the transfer phase. There are two types of transfer: First, lexical transfer, second, structural transfer.

The lexical transfer converts every word in the source sentence to corresponding target language representation (English word). The syntactic transfer convert the parse tree of the Arabic sentence to its equivalent in English. In addition, the bilingual dictionary is essentially in transfer method. Figure 3 illustrates an example of lexical transfer.

The structural transfer provides the rules for converting source language parser trees into equivalent target language trees (Trujillo, 1999). The transfer rules deals with the restructuring of the parse tree and reordering of words between the source and target language. Thus, this step provided the rules for convert Arabic parse tree into equivalent English tree. Figure 4 shows an example of the transfer step.

Generation: In this step, the parsing rules of the target Language is used to produce the target sentence. The generation stage is generally divided into two parts, syntactic generation and morphological generation. Syntactic generation: the English parse tree is traversed to produce the final structure of the English sentence.

Fig. 3:	Example of lexical transfer

Fig. 4:	Example of the transfer process

Fig. 5:	Example in generation stage

Morphological generation that generates inflected English word in its correct form based on a set of English grammar rules.

As an example, Fig. 5 shows how the generation rules added some English words (the, of, are) to produce the final structure of the English sentence:

Input	:
Transfer	:	Days tashreeq day eating and drinking
Output	:	The days of tashreeq are the days of eating and drinking

RESULTS AND DISCUSSION

In general, the rationale of this experiment is to investigate whether a machine translation system, Google and CArabicMT are sufficiently robust to be translated classical Arabic to English by comparing their output with the human translation. The test set is drawn randomly from a classic Arabic book “Sahih Muslim”.

The result of evaluation of translation has performed by counting out of the total score of each system for all sentence, score is a number between 0 and 10 (Roshidul et al., 2011; Jiang and Wei, 2012). Then, the score is divided it by the number of sentences multiplied by 10 to find their percentage out (Table 4).

The score is given by human expert in translation and it measures the differences between the human translation, Google and CArabicMT system. The range of scores is between 0 and 10 determines the correctness of the translation or matching translation according to the magnitude of error in the structure or the meaning of the generated English sentence. While 0 indicates an absolutely incorrect translation, 10 indicate absolutely correct (matched) translation.

The result was 49% for Google and 83.5% for CArabicMT.

Table 4:	Experiment results

Table 5:	Type of problem frequencies with classical Arabic to English MT

Table 6:	Shows part of the result produced by this experiment
Sentences have been tested by Google in December 2010

However, Table 5 represents all types of the problem returned by each of the examined systems, Google and CArabicMT and their frequencies. If we examined the first row for the Synonyms of a word will find that this type of problem frequented 36 times with Google and only 2 times with our system. Therefore, as a total this type of problem frequented 38 times with two systems. Table 6 show some examples which explain in details the evolution process.

CONCLUSION

This study has been concentrated on issues in the design and implementation of a machine translation system which translates a sentence from classical Arabic language to English based on rule-based approach. There are several reasons that make transfer based is desired by MT community (Trujillo, 1999). These reasons showed that the transfer based approach is promising. So that we can improve the quality of machine translation output and increases its usefulness in classical Arabic. However, we have faced some difficulties when building the system, the first difficulty is; most of the existing Arabic database does not contain words in classical Arabic. However, due to a difference in the sentence structure between MSA and classical Arabic, it makes the rules which developed by them are not appropriate for the classical Arabic. Features classical Arabic is short sentence, word affecting and rhetoric miracle. Therefore, the translation classical Arabic directly is miracles even for specialists, also that Scholars of Arabic language stood to explain the vocabulary and interpretation of grammatical compositions and the development of its implications. Brevity sentences and abundance of meaning make the translation of classical Arabic more complexes. Therefore, the translation classical Arabic needs to have more sophisticated analysis and to go deep to the semantic and pragmatic levels to interpret its meaning. We think that will be one of the most challenges to the Arabic computational linguists in the future. However, the translation machines of the literature language such as poems and novels still in its fancy stage even in the good studied languages such as English. In future works, there are more activities are needed to enlarge and enrich our system, so to as better handle more complicated situations. All concerned with extending our paper work to be more powerful and applicable. In what follows, we presented some Proposals: Prepare and build a Private database of religious domain that containing the words written in classical Arabic and in the religious, development of this system to deal with formation. In addition translation of classical Arabic by Semantic translation that is the process of using semantic information to aid in the translation of classical Arabic sentence to create an equivalent meaning of this sentence. Here, we just reported our modest effort to open the research in this direction for further enhancement.