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Table 1. Sample Closed POS Categories List International Journal on Natural Language Computing (IJNLC) Vol.11, No.5, October 2022

Table 1 Sample Closed POS Categories List International Journal on Natural Language Computing (IJNLC) Vol.11, No.5, October 2022

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MTE Tagger is totally based on readily available primitive data lists and a complex set of linguistic rules both of which are highlighted next:
MTE Tagger is totally based on readily available primitive data lists and a complex set of linguistic rules both of which are highlighted next:
Table 2. Gramatical, Morphological and Strctural Procedures (Rules) 5. EXPERIMENTS, EVALUATIONS, AND DISCUSSIONS The evaluations process consists of the following experiments with results as shown in Table 3. Two sets of experiments were performed. The first set is made of four runs on four different un- annotated data sets to compare performance (Accuracy and Timing) of the new tagger to that of Stanford Tagger.
Table 2. Gramatical, Morphological and Strctural Procedures (Rules) 5. EXPERIMENTS, EVALUATIONS, AND DISCUSSIONS The evaluations process consists of the following experiments with results as shown in Table 3. Two sets of experiments were performed. The first set is made of four runs on four different un- annotated data sets to compare performance (Accuracy and Timing) of the new tagger to that of Stanford Tagger.
Table 3. Accuracy and Timings results comparison. The second set of experiments are based on a small selected dataset that is manually annotated. The two taggers are both run on the data set and accuracy of tagging and speed of performance are noted and compared. Accuracy is a representation of the number of rightly tagged tokens while perfor- mance is the speed of tagging. Due to the expectation that rule-based systems tend to be much faster and robust, the measurements take are only indicative and lack features of a well-controlled experiments.
Table 3. Accuracy and Timings results comparison. The second set of experiments are based on a small selected dataset that is manually annotated. The two taggers are both run on the data set and accuracy of tagging and speed of performance are noted and compared. Accuracy is a representation of the number of rightly tagged tokens while perfor- mance is the speed of tagging. Due to the expectation that rule-based systems tend to be much faster and robust, the measurements take are only indicative and lack features of a well-controlled experiments.
Table 4. A sentence example: Made of 27 Tokens. Taggers match on 19 and mismatch on &
Table 4. A sentence example: Made of 27 Tokens. Taggers match on 19 and mismatch on &
International Journal on Natural Language Computing (IJNLC) Vol.11, No.5, October 2022
International Journal on Natural Language Computing (IJNLC) Vol.11, No.5, October 2022
Table 5. Overal missed percentage for the different POS catagories Looking at the overall success of tagging we could see that Adjectives (JJ) are the least accurate in MTE and better rules will still have to be invented to improve the classification of JJs.
Table 5. Overal missed percentage for the different POS catagories Looking at the overall success of tagging we could see that Adjectives (JJ) are the least accurate in MTE and better rules will still have to be invented to improve the classification of JJs.
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Computer ScienceArtificial IntelligenceNatural Language ProcessingTextile Chemical ProcessingNatural Language Processing Text MiningArabic Text Processing
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