Question analysis for Indonesian comparative question

Saelan, Athia; Purwarianti, Ayu; Widyantoro, Dwi H.

doi:10.1088/1742-6596/801/1/012077

Cited by 2 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental setting is still the same as [20], but we used the data described in Section 2-A -2-C. From the preliminary experiment, it is found that the information extraction first scenario is better than question classification first. For the information extraction process, we do token classification with these features: the word lexical, 2 words before and after, and the previous label.…”

Section: E Preliminary Results On Question Analysismentioning

confidence: 99%

“…We collect 477 comparative question for training and testing data, including 150 question from our previous work [20]. Some of the questions from the previous work are not used because it is still ambiguous whether the questions are comparative or not.…”

Section: Overview On Data and Question Analysis A Datamentioning

confidence: 99%

“…In our previous work [20], we described the question analysis process. We used information extraction and question classification techniques for the question analysis.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Answering Comparison in Indonesian Question Answering System with Database

Saelan¹,

Purwarianti²,

Widyantoro³

2018

IJEEI

View full text Add to dashboard Cite

In this research, we build a question answering system that can answer comparative questions. Our system contains two components, question analysis and answer processing. In question analysis, we use information extraction method to extract entities, aspects, relations, and constants from the question. We also classify the question into five question types such as entity-mentioned, entity-other, entity-all, aspect, and yes/no. These processes in question analysis component are solved by machine learning technique. In answer processing component, we process the comparison by using word lists and rules. The result of comparison processing is used to find the answer by generating query to get relevant data from the database. The query generation process is performed by using rules based on the question type. After that, the data from database is also processed based on question type to generate the answer. Based on the experiment results, our proposed method for comparison question answering system is promising.

show abstract

Section: E Preliminary Results On Question Analysismentioning

confidence: 99%

Section: Overview On Data and Question Analysis A Datamentioning

confidence: 99%

See 1 more Smart Citation

Answering Comparison in Indonesian Question Answering System with Database

Saelan¹,

Purwarianti²,

Widyantoro³

2018

IJEEI

View full text Add to dashboard Cite

show abstract

“…Some works on mining comparative opinion have been performed in other languages, such as Chinese [26][27][28][29], Korean [30,31], Vietnamese [32] and Indonesian [33].…”

Section: Comparative Opinion Miningmentioning

confidence: 99%

Identifying comparative opinions in Arabic text in social media using machine learning techniques

Alharbi

Khan

2019

SN Appl. Sci.

View full text Add to dashboard Cite

Social networking sites have become an integral part of everyday life, where people interact, cooperate and quarrel with each other. Social media also encourages them to express their opinions and share their comments about their lives' events or about the product they use. Opinions can be direct without any comparison (I like ABC phone) or they can be comparative (X-phone's camera is better than Y-phone). Comparative opinions are useful in many applications, e.g. marketing intelligence, product benchmarking, and e-commerce. The automatic mining of comparative opinions is an important text mining problem and an area of increasing interest for different languages. This paper focuses on identification of comparative sentence from non-comparative ones in Arabic texts. A corpus was developed consisting of YouTube comments. This paper describes research experiments that aimed to apply data/text mining algorithms, natural language processing and linguistic classification for Arabic comparative text discovery. The results of these experiments along with the analysis are also presented. The results were promising reaching to 91% accuracy for the detection of comparative opinions.

show abstract

Question analysis for Indonesian comparative question

Cited by 2 publications

References 4 publications

Answering Comparison in Indonesian Question Answering System with Database

Answering Comparison in Indonesian Question Answering System with Database

Identifying comparative opinions in Arabic text in social media using machine learning techniques

Contact Info

Product

Resources

About