#TITLE_ALTERNATIVE# - CHANDRA RAJAGUKGUK (NIM : 13514082), RIO
The semantic similarity between two pieces of text (STS: Semantic Textual Similarity) can be represented by a number. STS aims to measure the degree of semantic similarity to the pair of text pieces. However, the number representation raises assumption to give semantic meaning of the sentence, no more specific explanation based on the number of similarities. From this problem requires an explanation of why the two sentences are to be similar or otherwise. Interpretable Semantic Textual Similarity (iSTS) is a task that give those needs, to explain the semantic resemblance of two sentences. The output of iSTS is a pair of chunks with its relation scores and labels. The score ranged 0 to 5, while the labels are EQUI, SPE1, SPE2, OPPO, REL, SIMI, and NOALI. However, the iSTS corpus is currently limited to only a few languages, excluding Indonesian. The purpose of this final project is to build iSTS model based on Indonesian language corpus and also the corpus is built on this final project. In this final project, two best current iSTS techniques for English are VRep and UWB. The VRep technique uses WordNet to represent word semantics, while UWB uses word embeeding. Both of these techniques use the same general step of preprocessing, feature extraction, and classification. The difference between these two techniques lies in slightly different preprocesses and unique feature extraction methods. Adaptation of VRep and UWB in this final project is done in the preprocessing stage, feature extraction, and classification with four machine learning techniques used decision tree, SVM, random forest, and multilayer perceptron. Based on F1 evaluation on the type, score, and type + score aspect, the best iSTS model in VRep technique is SVM for the type aspect with F1 test of 0.7037, decision tree for score aspect with F1 test 0.8770 and SVM for score + F1 test 0.6821. While in UWB obtained the best decision tree iSTS model on aspect type with F1 test 0.6869, desicion tree on aspect score with F1 test 0.8886, and SVM for aspect type + score with F1 test 0.6821. In this final project, VRep becomes the best model for aspect type and score, UWB for aspect type + score.