Self-Learning System With Natural Question-Guided Search
For Narration In Indonesian Language
Edwin Wibowo Sampurna
1
, Esther Irawati Setiawan
2
Global Business, Chinese Culture University 55, Hwa-Kang Road, Taipei, Taiwan R.O.C 11114
1
edwin.sampurnagmail.com Sekolah Tinggi Teknik Surabaya
Ngagel Jaya Tengah 73-77, Surabaya, Indonesia
2
esther.irawatigmail.com
Abstract The need of self-learning education
is quite high recently. Peoples find how to do self-learning with the easiest way. That is one
reason for us to research more about Natural Question-Guided Search and try to make a
system that can help us for self-learning education. This System is made into self-
learning
education using
the Natural
Question-Guided Search algorithm to make a question that can form a question along with
answers that can be used to improve the self- learning understanding. Natural Question-
Guided Search is an algorithm used to make a question from a narrative sentence.
This Natural Question-Guided Search utilizes a Dependency Parser to process a sentence.
Dependency Parser is a syntactical analysis process of a sentence which assume that a
word is dependent on other words head. This relation shows grammatical function between
words
which cannot
be shown
by constituency-based parser that can only
identify phrase. By knowing grammatical function between those words, it will ease the
creation of a question. A test is done to determine the accuracy of the
application of this Natural Question-Guided Search algorithm with applying it to the school
reading text book to create a question.
Keywords natural language processing,
natural question-guided search, dependency parser, constituency parser, bahasa Indonesia
1. I
NTRODUCTION
Nowadays, the knowledge obtained from the learning process is really important. In addition
to reading, comprehension of the text can be improved by doing an exercise question of the
text. It will be able to increase the absorptive capacity of comprehension about the text. But,
many narrative text that does not include an exercise questions that are useful to test the
readers understanding of the text.
With the technology that we know is increasing everyday, it is needed a system that
can make such a question directly from an existing narrative texts, so that the reader will get
a question and can answer any questions after reading the narrative text in order to improve
understanding and comprehension of the content of the text. With that way, the reader can study or
learn a text easily by themselves.
Natural Question-Guided Search algorithm is require to generate a question. Natural Question-
Guided Search is an algorithm developed by Alexander Kotov and ChengXiangZai by using
Dependency Parsing to process a sentence and generate a tree as the output. This output is
Dependency Tree which further will be processed by Natural Question-Guided Search algorithm to
generate a question. Those questions can be use for self-learning education that can help reader to
study or learn a text by themselves.
2. F
UNDAMENTAL
2.1. Indonesian Language Pattern
The same with English, Indonesian Language Pattern also consist of phrase, clause and
sentence. Clause is consist of two or more words that
make up a construction containing elements of the predicative, and has the potential to be a
sentence.
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Phrase is consist of two words or a more words that forms a constituent and by which it functions
as a single unit in the syntax of a sentence. Sentence is a group of words that are put
together to mean something which expresses a complete thought. It does this by following the
grammatical rules of syntax. There are four type of Sentence: Simple Sentence, Compound
Sentence, Complex Sentence and Complex- Compound Sentence. But on this research, it is
focus only on Simple Sentence.
2.2. Natural Language Processing
Natural Language Processing NLP that is a part of computer science, Artificial Intelligence
AI, and Linguistics which deals with the interaction between human and computer so that
the computer has the ability to be able to understand natural human language.
There are four NLP that used for this research : Part-of-Speech Tagging POS Tagging, Named
Entity Recognition, Constituency Parsing dan Dependency Parsing.
2.2.1. Part-Of-Speech Tagging
Part-of-Speech Tagging POS Tagging is the process of determining the words according to
the grammar for each of the words in the sentences of natural language. POS Tagging is
also can provide information of the word from syntactic or morphology of a sentence.
This research use Indonesian Language POS Tagging iPOSTagger that made by Alfan
Farizki Wicaksono and Ayu Purwarianti. iPOSTagger is very important in this research,
especially in classified and tagging the words on a sentence.
2.2.2. Named Entity Recognition
Named Entity Recognition NER is one of the components of information extraction to detect
and classify the named-entity in a text. NER is generally used to detect peoples names, place
names and organization of a document.
This research use LingPipe NER for English. in this reasearch, so that LingPipe NER can be
use. While for detect the other names will be use some addition rules for categorizing.
2.2.3. Constituency Parsing
Constituency Parsing or Phrase Structure Grammar is a parser used in NLP to parse a
sentence. The function of this parser is as decomposers sentence to make a Constituency
Tree or Phrase Structure Grammar Tree Tree Grammar Pattern. Constituency Parser using the
grammar rules to generate Constituency Tree of a sentence so that it becomes a model of grammar
patterns.
But Constituency Parsing is not developed in this research. This research use Constituency
Parsing by Stanford Parser to generate a Constituency Tree. Stanford Parser is an English
parser. Since Indonesian Language has the similarities with English in terms of sentence
structure, the Stanford Parser can be used.
2.2.4. Dependency Parsing
Dependency Parsing is a parser which generates a grammar that describes the
dependence between the components which one is the head and the other is dependent. Head also
called modifier as a determinant for the partner.
Dependency Parsing will be done using the method of mapping from Constituent Structure to
Dependencies Structure, because the input to this parser is a constituent-based sentence that is
output from last process.
2.3. Natural Question-Guided Search
Natural Question-Guided
Search is
an algorithm that can transform a narrative text into
a question. The first idea of development this algorithm
is by
Alexander Kotov
and Chengxiang Zhai actually is due to the usual
information retrieval due to the need of an answer for a question. Questions that written
with good grammar patterns will yield a better and faster result. In general, it is conceivable that
Natural Question-Guided Search algorithm is very useful to for search an information by
generate a good question.
But in this research, Natural Question-Guided Search algorithm not use to generate a question
that use to search an information to get a better and faster result, but this algorithm is only use to
generate a question with the answer, so it can be use for self-learning study.
There are two early stage should be done before running this algorithm, Pre-processing
stage, and the second is Dependency Parsing. Furthermore, Dependency Tree output from
Dependency Parsing will be processed by Question-Guided Natural Search algorithm.
The complete Question Forming Process Diagram can be seen on Fig. 1.
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Fig. 1 Question Form Phase Processing Diagram
3. Q
UESTION
F
ORM
P
ROCESSING
The process of Question Form Processing is processed through two main stages, Analysis
Solution Dependency Parsing and Natural Question-Guided Search.
