# A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences
## 3 Proposed Framework
### Arabic Comparative Sentence Type Identification
As previously described, the proposed REACS algorithm extracts the following relation components from a comparative sentence if all or some of them are applicable to the sentence type. The components are the relation word, the extracted feature, entity 1, entity 2 and the preferred entity. Figure 4 and Table 7 show the precision of each of the relation components for the four types of the Arabic comparative sentences. The precision was calculated using Equation (1). The TP and FP parameters mentioned in the equation indicate the number of comparative sentences in which the relation components were truly and falsely identified, respectively. Each comparative sentence type has a separate subfigure in Figure 4.

The evaluation results of the REACS algorithm show that the relation word was truly identified by almost 100% in all Arabic comparative sentence types using all datasets. In general, the three evaluated algorithms provided the highest precision in the MSA dataset when compared with the other two datasets for all sentence types. This finding applies to almost all relation components. The detailed discussion of the low precision results that occur in Figure 4 are presented later in the next two sections. Figure 4: Graphical evaluation results of REACS, PEEANCS and PEEASCS algorithms for each comparative sentence type, (**a**) Non-equal gradable, (**b**) Equative, (**c**) Superlative, and (**d**) Non-gradable. #### 5.3.1. Discussion of REACS Algorithm Evaluation Results

There are some limitations to the Arabic language which may cause false identification results of the REACS algorithm. Such limitations include the following. * In some Arabic language sentences, each of entity 1 and entity 2 consists of more than one word. Each entity can be formed of several words; however, it is considered only one element in the relation extraction process. This results in extracting false relation elements, i.e., entity 1, entity 2 and feature. * The difference in the sentence order can affect the correct extraction of relation components. For example, in the superlative comparative sentence type, the relation word can exist in the beginning of the comparative sentence. An example of this in the sentence _case_, _case_ (the first license), the relation word is \(\bigcup_{\textbf{3}}\)! (the first) and entity 1 is _case_ (license). However, in other sentence, the relation word may exist in the middle of the sentence and entity 1 can exist in the beginning. For example, in the sentence _case_ (the countries are in the weakest situation), the relation word is _case_ (the weakest) and entity 1 is _case_ (the countries). A third example, is in the sentence _case_ (the hardest thing about it is ambition), where the relation word is _case_ (the hardest) and entity 1 is _case_ (ambition), which exists by the end of the sentence. * Entity 1 can be itself a comparative sentence and can exist by the end of the main comparative sentence such as the sentence _case_ (the closest to reality is the most knowledgeable of the benefits). In this sentence, the relation word of the main comparative sentence is _case_ (the closest) and entity 1 is _case_ (the most knowledgeable of the benefits). * The correct relation extraction from some Arabic sentences mainly depends on precise understanding of the meaning and the context of the sentence such as _case_ (the song named \"the most wonderful years in our life\"). In this sentence, the relation word is _case_ (the most wonderful years in our life\") not _case_ (the song).