scholarly journals A simple rule-based part of speech tagger

1992 ◽  
Author(s):  
Eric Brill
Keyword(s):  
Simple Rule ◽  
Rule Based ◽  
Part Of Speech

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 Pengaruh Part of Speech Tagging Berbasis Aturan dan Distribusi Probabilitas Maximum Entropy untuk Bahasa Jawa Krama

2016 ◽  
Vol 7 (4) ◽  
Author(s):  
Hafiz Ridha Pramudita ◽  
Ema Utami ◽  
Armadyah Amborowati
Keyword(s):  
Maximum Entropy ◽  
Syntactic Category ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Speech Tagging ◽  
Local Languages

Abstract. Javanese language is one of the local languages in Indonesia, which is used by most of the population of Indonesia. The language has complex grammar to embrace the values of decency that is determined by the use of words containing courtesy known as Raos Alus. Every word in the Javanese belongs to a certain part of speech like what happens to other languages. Part of Speech (POS) tagging is a process to set syntactic category in a word such as nouns, verbs, or adjectives to every word in the document or text. This study examined the POS Tagging with Maximum Entropy and Rule Based for Javanese Krama—Higher Javanese--by using the Open NLP library to measure the maximum entropy. The results obtained are Maximum Entropy and Rule Based can be used for POS Tagging on Javanese Krama with the highest accuracy of 97.67%.Keywords: POS Tagging, NLP, Maximum Entropy, Rule Based, Javanese Krama LanguageAbstrak. Bahasa Jawa merupakan salah satu bahasa daerah di Indonesia yang dipakai oleh sebagian besar penduduk Indonesia. Bahasa Jawa memiliki tata bahasa yang kompleks karena menganut nilai-nilai kesopanan yang ditentukan berdasarkan penggunaan dengan kata-kata yang mengandung raos alus (rasa sopan). Setiap kata dalam Bahasa Jawa memiliki jenis kata atau part of speech tertentu seperti halnya dengan bahasa-bahasa lain. POS tagging merupakah bagian penting dari cakupan bidang ilmu Natural Languange Processing (NLP). Penelitian ini menguji POS Tagging dengan Berbasis Aturan dan distribusi probabilitas Maximum Entropy pada Bahasa Jawa Krama menggunakan library OpenNLP untuk mengukur maximum entropy. Hasil yang diperoleh adalah Maximum Entropy dan Rule Based dapat digunakan untuk POSTagging pada Bahasa Jawa Krama dengan akurasi tertinggi 97,67%.Kata Kunci: POS Tagging, NLP, Maximum Entropy, Rule Based, Bahasa Jawa Krama

scholarly journals Preliminary prediction of the potential distribution and consequences of Haemaphysalis longicornis using a simple rule-based climate envelope model

2018 ◽  
Author(s):  
Krisztian Magori
Keyword(s):  
Atlantic Coast ◽  
The United States ◽  
Simple Rule ◽  
Potential Range ◽  
Haemaphysalis Longicornis ◽  
Rule Based ◽  
The Us ◽  
Climate Envelope ◽  
Envelope Model ◽  
The Many

AbstractHaemaphysalis longicornis, the Asian longhorned (or bush) tick has been detected on a sheep in August 2017 in Hunterdon County, New Jersey. By October 26, 2018, this tick has been detected in 44 counties in 9 states along the Atlantic coast of the United States, with the first detection backdated to 2010. Here, I use a simple rule-based climate envelope model, based on a prior analysis in New Zealand, to provide a preliminary analysis of the potential range of this introduced tick species in North America. After validating this model against the counties where the tick has been already detected, I highlight the counties where this tick might cause considerable economic harm. I discuss the many limitations of this simple approach, and potential remedies for these limitations, and more sophisticated approaches. Finally, I conclude that substantial areas of the US, especially along the Gulf and Atlantic coast, are suitable for the establishment of this tick, putting millions of heads of livestock potentially at risk.

Constructing a Rule Engine System with SQL Server 2008

2010 ◽  
Vol 20-23 ◽  
pp. 1072-1077
Author(s):  
Zhi Jun Ren
Keyword(s):  
Sql Server ◽  
Simple Rule ◽  
State Machines ◽  
Business Rules ◽  
Rule Engine ◽  
Rule Based ◽  
Rule Based System ◽  
Engine Design ◽  
Engine System

Rules play a central role in a wide variety of applications. In addition to the declarative specification of business rules, the simple rule engine design described in this article can be used to implement state machines, predicate dispatchers, or any other rule-based system. This paper introduces how to design a Rule Engine System with SQL Server 2008.

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.

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