ESTIMATION QUESTION TYPE ANALYZER FOR MULTI CLOSE DOMAIN INDONESIAN QAS

We propose an automated estimation scheme to analyze question classification in Indonesian multi closed domain question answering systems. The goal is to provide a good questioning classification system even if using only available language sources. Our strategy here is to build a pattern and rule to extract some important words and utilize the results as a feature for classification estimation of automated learning-based questions. Scenarios designed in automated learning estimates: (i) Analyzing questions, to represent the key information needed to answer user questions using target focus and target identification; (ii) Classify the type of question, construct a taxonomy of questions that have been coded into the system to determine the expected answer type, through some question processing patterns and rules. The proposed method is evaluated using datasets collected from various Indonesian websites. Test results show that the classification process using the proposed method is very effective.  Question classification is an important phase in most question answering systems. There are three approaches for question classification : rule based, language modeling and machine learning based. The questions classification of rule-based uses a set of standard heuristic rules based on taxonomy. Rule-based approaches classify questions using rules created manually by experts. Rule-based classification uses rules to detect keyword questions and utilize Word Net to map target categories . Rule-based approaches do not support other domains or different languages because it is difficult to create a new set of rules framework. Rule-based approach performs well in specified data sets and not on the degradation of new data set performance . Rulebased approaches are accurate in predicting certain categories of questions. However, this is not measurable for a large number of questions and syntactic structures. Rule-based questioning classification studies focus more on using the syntax and relationship rules between words [5][6], rules of relation and determines layer taxonomy by determining coarse class [7] . Machine learning focuses on developing computer programs that can teach themselves to grow and change. Machine learning provides potential solutions across these domains and more . There have been many research classification questions with approaches to machine learning such as Purwarianti et al  proposing a shallow parser to take some important words and utilize the results as a feature for classification of SVM-based questions.  uses the SVM algorithm with a composite feature of word categories and focus questions using a syntactic semantic structure. Zhang [11] and Mishra [13] compared various methods of machine learning to classify questions such as Nearest Neighbors, Naive Bayes, Decision Tree, Wicked Networks from Winnows (SnoW) and Support Vector Machine (SVM) using bag-of-word and n-gram features. The questions classification is analyzed by various approaches from the point of view of previous researchers to improve class classification for the better. Although the learning engine approach is better but the rule-based approach has its own challenge: how to improve the system feature automatically to produce some complex information so that the classification becomes true. Based on that in this paper will propose the scheme of [Suwarningsih * et al., 4(6): June, 2017] ISSN: 234-5197 Impact Factor: 2.715 INTERNATIONAL JOURNAL OF RESEARCH SCIENCE &  classification of questions using a combination approach of template patterns with the rules of relations between words. The generation of inter-word relationship rules uses the learning concept of knowledge based on the variation of the close domain. The proposed contributions are new scheme for Indonesian multi close domains that can be used to such as generate question template patterns and Expected Answer Type (EAT) variations. The rest of the paper is organized as follows: Section. 2 describes several related work on question classification rule-based; Section 3 proposed method an estimation automatic learning for question analysis and question classification; Section. 4 discusses the learning issues involved in QC and presents our learning approach. Related Work The research on the question classification of intensive has been done by many researchers but the question classification is still a trend of continuous research review. This is based on several considerations such as how large the system is able to define the expected type of response [4][6], how large the system is capable of sorting words that include stop-world automatically in the classification of questions   and how the system is able to handle variations of question forms to produce accurate high . Especially rule-based classification has its own challenge: how to improve system features automatically to generate some complex information so that classification becomes true. There are many question classification researches with rule-based questioning typically use word extraction functions, syntax patterns and build relationships between words. Such as Te, et al [13] focuses on word extraction and establishing the rules of relations between words using Tibetan. The proposed method is to collect information by comparing keywords. This strategy is done to reduce the search space and improve the efficiency of the word search process. The results of this approach are able to initialize the knowledge base with the relation rules for the Tibetan language question structure. Sarrouti et al  proposed an effective and efficient method for Biomedical QTs Classification. They have classified the Biomedical Questions into three broad categories and defined the Syntactic Patterns for particular category of Biomedical Questions. Therefore, using these Biomedical Question Patterns, they have proposed an algorithm for classifying the question into particular category. The proposed method was evaluated on the Benchmark datasets of Biomedical Questions. The experimental results show that the proposed method can be used to effectively classify Biomedical Questions with higher accuracy.  develop a rule based system Quarc that can read a short story and find the sentence in the story that best answer a given question. Quarc uses heuristic rules that look for lexical an semantic rules in the question and the story. Each rule awards a certain number of points to a sentences. After all of the rules have been applied, the sentence that obtains the highest score is returned as the answer. Haris & Omar [5] describes a rule-based approach to analyze and classify written examination questions through natural language processing for computer programming subjects. In general, Bloom's Taxonomy or the Taxonomy of Educational Objectives (TEO) acts as a main guideline in assessing a student's cognitive level. However, academicians need to design the appropriate questions and categorize it to the cognitive level of TEO manually. Biswas et al  proposed a compact and effective method for question classification. Here rather than using a two layered taxonomy of 6 course grain and 50 fine grained categories developed by Li and Roth [7]. They have classified the questions into three broad categories studied the syntactic structure of the question and suggest the syntactic patterns and expected answer type for particular category of questions. Using these question Patterns they have also suggested an algorithm for classifying the question into particular category. They have also studied the syntactic structure of the question and suggest the syntactic patterns and expected answer type for particular category of questions [Suwarningsih * et al., 4(6): June, 2017] ISSN: 234-5197 Impact Factor: 2.715 INTERNATIONAL JOURNAL OF RESEARCH SCIENCE &  Proposed Work Here, we would like to show our framework of Question processing with method an estimation automatic learning for question analysis, question classification and automatic extraction learning. Framework of proposed method that we have built can be seen in Figure 1. Parsing rsi Extraction and relation word learning tr cti r l ti r l r i datasets collected from various Indonesian Domain t s ts c ll ct fr ri s