An Automatic Question Generation System using Rule-Based Approach in Bloom’s Taxonomy

2019 ◽  
Vol 13 ◽  
Author(s):  
G Deena ◽  
K Raja ◽  
K Kannan
Keyword(s):  
Language Processing ◽  
Learning Process ◽  
Question Generation ◽  
Test Question ◽  
Rule Based ◽  
Part Of Speech ◽  
Core Idea ◽  
Rule Based Approach ◽  
Teaching Learning ◽  
Automatic Question Generation

: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.

scholarly journals Automatic Question Generation and Evaluation

2021 ◽  
Vol 23 (05) ◽  
pp. 751-761
Author(s):  
Parth Panchal ◽  
◽  
Janak Thakkar ◽  
Veerapathiramoorthy Pillai ◽  
Shweta Patil ◽  
...  
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Generation Process ◽  
Question Generation ◽  
Machine Learning Classification ◽  
Pos Tagging ◽  
Text Document ◽  
Rule Based Approach ◽  
Automatic Question Generation

Generation of questions from an extract is a very tedious task for humans and an even tougher one for machines. In Automatic Question Generation (AQG), it is extremely important to examine the ways in which this can be achieved with sufficient levels of accuracy and efficiency. The way in which this can be taken ahead is by using Natural Language Processing (NLP) to process the input and to work with it for AQG. Using NLP with question generation algorithms the system can generate the questions for a better understanding of the text document. The input is pre-processed before actually moving in for the question generation process. The questions formed are first checked for proper context satisfaction with the context of the input to avoid invalid or unanswerable question generation. It is then preprocessed using various NLP-based mechanisms like tokenization, named entity recognition(NER) tagging, parts of speech(POS) tagging, etc. The question generation system consists of a machine learning classification-based Fill in the blank(FIB) generator that also generates multiple choices and a rule-based approach to generate Wh-type questions. It also consists of a question evaluator where the user can evaluate the generated question. The results of these evaluations can help in improving our system further. Also, evaluation of Wh questions has been done using the BLEU score to determine whether the automatically generated questions resemble closely the human-generated ones. This system can be used in various places to help ease the question generation and also at self-evaluator systems where the students can assess themselves so as to determine their conceptual understanding. Apart from educational use, it would also be helpful in building chatbot-based applications. This work can help improve the overall understanding of the level to which the concept given is understood by the candidate and the ways in which it can be understood more properly. We have taken a simple yet effective approach to generate the questions. Our evaluation results show that our model works well on simpler sentences.

scholarly journals A Study on Text Rank Algorithm for Automatic Text Summarization and Question Generation

2021 ◽  
pp. 24-28
Author(s):  
G. Deena
Keyword(s):  
Design System ◽  
Question Generation ◽  
Automated Evaluation ◽  
Automatic Text Summarization ◽  
Human Evaluation ◽  
The People ◽  
Rule Based Approach ◽  
Automatic Question Generation ◽  
Automatic Text ◽  
Better Than

This paper proposes a new rule-based approach to automated question generation. The proposed approach focuses on the analysis of both sentence syntax and semantic structure. The design and implementation of the proposed approach is also described in detail. Although the primary purpose of a design system is to generate query from sentences, automated evaluation results show that it can also perform great when reading comprehension datasets that focus on question output from paragraphs. With regard to human evaluation, the designed system performs better than all other systems and generates the most natural (human-like) questions. We present a fresh approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems. In our system, we will take data from various sources for a particular topic and summarize it for the convenience of the people, so that they don't have to go through so multiple sites for relevant data.

A FINITE STATE COMMA TAGGER

2004 ◽  
Vol 13 (03) ◽  
pp. 449-468 ◽  
Author(s):  
SEBASTIAN VAN DELDEN ◽  
FERNANDO GOMEZ
Keyword(s):  
Learning Algorithm ◽  
Finite State Automata ◽  
Rule Based ◽  
Domain Specific ◽  
Part Of Speech ◽  
System A ◽  
Finite State ◽  
Rule Based Approach

A method has been developed and implemented that assigns syntactic roles to commas. Text that has been tagged using a part-of-speech tagger serves as the input to the system. A set of Finite State Automata first assigns temporary syntactic roles to each comma in the sentence. A greedy learning algorithm is then used to determine the final syntactic roles of the commas. The system requires no training and is not domain specific. The performance of the system on numerous corpora is given and compared against a rule-based approach.

scholarly journals PENENTUAN KELAS KATA PADA PART OF SPEECH TAGGING KATA AMBIGU BAHASA INDONESIA

2018 ◽  
Vol 2 (3) ◽  
pp. 157
Author(s):  
Ahmad Subhan Yazid ◽  
Agung Fatwanto
Keyword(s):  
Language Processing ◽  
Word Class ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Ambiguous Words ◽  
Computer Science Faculty ◽  
Speech Tagging ◽  
Bahasa Indonesia

Indonesian hold a fundamental role in the communication. There is ambiguous problem in its machine learning implementation. In the Natural Language Processing study, Part of Speech (POS) tagging has a role in the decreasing this problem. This study use the Rule Based method to determine the best word class for ambiguous words in Indonesian. This research follows some stages: knowledge inventory, making algorithms, implementation, Testing, Analysis, and Conclusions. The first data used is Indonesian corpus that was developed by Language department of Computer science Faculty, Indonesia University. Then, data is processed and shown descriptively by following certain rules and specification. The result is a POS tagging algorithm included 71 rules in flowchart and descriptive sentence notation. Refer to testing result, the algorithm successfully provides 92 labeling of 100 tested words (92%). The results of the implementation are influenced by the availability of rules, word class tagsets and corpus data.

scholarly journals Developing Core Technologies for Resource-Scarce Nguni Languages

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 520
Author(s):  
Jakobus S. du Toit ◽  
Martin J. Puttkammer
Keyword(s):  
South African ◽  
Language Processing ◽  
Rule Based ◽  
Local Resource ◽  
African Languages ◽  
Linguistic Resources ◽  
Part Of Speech ◽  
Parallel Data ◽  
Further Development

The creation of linguistic resources is crucial to the continued growth of research and development efforts in the field of natural language processing, especially for resource-scarce languages. In this paper, we describe the curation and annotation of corpora and the development of multiple linguistic technologies for four official South African languages, namely isiNdebele, Siswati, isiXhosa, and isiZulu. Development efforts included sourcing parallel data for these languages and annotating each on token, orthographic, morphological, and morphosyntactic levels. These sets were in turn used to create and evaluate three core technologies, viz. a lemmatizer, part-of-speech tagger, morphological analyzer for each of the languages. We report on the quality of these technologies which improve on previously developed rule-based technologies as part of a similar initiative in 2013. These resources are made publicly accessible through a local resource agency with the intention of fostering further development of both resources and technologies that may benefit the NLP industry in South Africa.

A Rule-Based Approach for Marathi Part-of-Speech Tagging

2021 ◽  
pp. 773-785
Author(s):  
P. Kadam Vaishali ◽  
Khandale Kalpana ◽  
C. Namrata Mahender
Keyword(s):  
Rule Based ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Rule Based Approach ◽  
Speech Tagging

Location Named-Entity Recognition using Rule-Based Approach for Balinese Texts

2021 ◽  
Vol 9 (3) ◽  
pp. 435
Author(s):  
Ni Putu Ayu Sherly Anggita S ◽  
Ngurah Agus Sanjaya ER
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Main Task ◽  
Rule Based ◽  
Personal Names ◽  
Named Entity ◽  
Proposed Model ◽  
Rule Based Approach ◽  
F Measure

In Natural Language Processing (NLP), Named Recognition Entity (NER) is a sub-discussion widely used for research. The NER’s main task is to help identify and detect the entity-named in the sentence, such as personal names, locations, organizations, and many other entities. In this paper, we present a Location NER system for Balinese texts using a rule-based approach. NER in the Balinese document is an essential and challenging task because there is no research on this. The rule-based approach using human-made rules to extract entity name is one of the most famous ways to extract entity names as well as machine learning. The system aims to identify proper names in the corpus and classify them into locations class. Precision, recall, and F-measure used for the evaluation. Our results show that our proposed model is trustworthy enough, having average recall, precision, and f-measure values for the specific location entity, respectively, 0.935, 0.936, and 0.92. These results prove that our system is capable of recognizing named-entities of Balinese texts.

Punjabi Pos Tagger: Rule Based and HMM

2017 ◽  
Vol 7 (7) ◽  
pp. 193
Author(s):  
Umrinderpal Singh ◽  
Vishal Goyal
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
State Of The Art ◽  
Input Word ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Unseen Data ◽  
Pos Tagger ◽  
Speech Tagging

The Part of Speech tagger system is used to assign a tag to every input word in a given sentence. The tags may include different part of speech tag for a particular language like noun, pronoun, verb, adjective, conjunction etc. and may have subcategories of all these tags. Part of Speech tagging is a basic and a preprocessing task of most of the Natural Language Processing (NLP) applications such as Information Retrieval, Machine Translation, and Grammar Checking etc. The task belongs to a larger set of problems, namely, sequence labeling problems. Part of Speech tagging for Punjabi is not widely explored territory. We have discussed Rule Based and HMM based Part of Speech tagger for Punjabi along with the comparison of their accuracies of both approaches. The System is developed using 35 different standard part of speech tag. We evaluate our system on unseen data with state-of-the-art accuracy 93.3%.

scholarly journals A Machine Translation System from Hindi to Sanskrit Language using Rule based Approach

2020 ◽  
Vol 21 (3) ◽  
pp. 543-554
Author(s):  
Neha Bhadwal ◽  
Prateek Agrawal ◽  
Vishu Madaan
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Translation System ◽  
Linguistic Features ◽  
Rule Based ◽  
Pragmatic Analysis ◽  
Machine Translation System ◽  
Iot Devices ◽  
And Mathematics ◽  
Rule Based Approach

Machine Translation is an area of Natural Language Processing which can replace the laborious task of manual translation. Sanskrit language is among the ancient Indo-Aryan languages. There are numerous works of art and literature in Sanskrit. It has also been a medium for creating treatise of philosophical work as well as works on logic, astronomy and mathematics. On the other hand, Hindi is the most prominent language of India. Moreover,it is among the most widely spoken languages across the world. This paper is an effort to bridge the language barrier between Hindi and Sanskrit language such that any text in Hindi can be translated to Sanskrit. The technique used for achieving the aforesaid objective is rule-based machine translation. The salient linguistic features of the two languages are used to perform the translation. The results are produced in the form of two confusion matrices wherein a total of 50 random sentences and 100 tokens (Hindi words or phrases) were taken for system evaluation. The semantic evaluation of 100 tokens produce an accuracy of 94% while the pragmatic analysis of 50 sentences produce an accuracy of around 86%. Hence, the proposed system can be used to understand the whole translation process and can further be employed as a tool for learning as well as teaching. Further, this application can be embedded in local communication based assisting Internet of Things (IoT) devices like Alexa or Google Assistant.

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