CTSS: A Tool for Efficient Information Extraction with Soft Matching Rules for Text Mining

Christy, A.; Thambidurai, P.

doi:10.3844/jcssp.2008.375.381

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…As proposed by Christy and Thambidurai (2008), additional algorithms can be useful in performing IE process. Using OMAMC technique logic, input keywords from user can be transformed into several keywords varieties (in respect to OMAMC format) in order to make retrieval process results have the value of precedence (based on the matched keywords categories).…”

Section: Resultsmentioning

confidence: 99%

One-Match and All-Match Categories for Keywords Matching in Chatbot

Lokman

Zain

2010

American Journal of Applied Sciences

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Problem statement: Artificial intelligence chatbot is a technology that makes interactions between men and machines using natural language possible. From literature of chatbot's keywords/pattern matching techniques, potential issues for improvement had been discovered. The discovered issues are in the context of keywords arrangement for matching precedence and keywords variety for matching flexibility. Approach: Combining previous techniques/mechanisms with some additional adjustment, new technique to be used for keywords matching process is proposed. Using newly developed chatbot named ViDi (abbreviation for Virtual Diabetes physician which is a chatbot for diabetes education activity) as a testing medium, the proposed technique named One-Match and All-Match Categories (OMAMC) is being used to test the creation of possible keywords surrounding one sample input sentence. The result for possible keywords created by this technique then being compared to possible keywords created by previous chatbot's techniques surrounding the same sample sentence in matching precedence and matching flexibility context. Results: OMAMC technique is found to be improving previous matching techniques in matching precedence and flexibility context. This improvement is seen to be useful for shortening matching time and widening matching flexibility within the chatbot's keywords matching process. Conclusion: OMAMC for keywords matching in chatbot is shown to be an improvement over previous techniques in the context of keywords arrangement for matching precedence and keywords variety for matching flexibility.

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Section: Resultsmentioning

confidence: 99%

One-Match and All-Match Categories for Keywords Matching in Chatbot

Lokman

Zain

2010

American Journal of Applied Sciences

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“…Another challenge when faced with data extraction especially in software engineering tasks is the fact that mostly, the developers use jargons which are not part of standard dictionaries. When one observes the CTSS tool (Christy and Thambidurai, 2008) for instance, the tool offers a lot of guarantees in terms of short processing time and the fact that different underlying modeling techniques are employed in its deployment. Some of the techniques are passing and soft matching rules.…”

Section: Unstructured Data Miningmentioning

confidence: 99%

Data Mining From Document-Append Nosql

Lomotey¹

2014

STSC

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Due to the unstructured nature of modern digital data, NoSQL storages have been adopted by some enterprises as the preferred storage facility. NoSQL storages can store schema-oriented, semi-structured, schema-less data. A type of NoSQL storage is the document-append storage (e.g., CouchDB and Mongo) which has received high adoption due to its flexibility to store JSON-based data and files as attachment. However, the ability to perform data mining tasks from such storages remains a challenge and the required tools are generally lacking. Even though there is growing interest in textual data mining, there is huge gap in the engineering solutions that can be applied to document-append storage sources. In this work, we propose a data mining tool for term association detection. The flexibility of our proposed tool is the ability to perform data mining tasks from the document-source directly via HTTP without any copying or formatting of the existing JSON data. We adapt the Kalman filter algorithm to accomplish macro tasks such as topic extraction, term organization, term classification and term clustering. The work is evaluated in comparison with existing textual mining tools such as Apache Mahout and R with promissory result on term extraction accuracy.

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Enhancement Algorithms for SQL-Based Chatbot

Lokman

Zain

2011

Software Engineering and Computer Systems

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CTSS: A Tool for Efficient Information Extraction with Soft Matching Rules for Text Mining

Cited by 5 publications

References 19 publications

One-Match and All-Match Categories for Keywords Matching in Chatbot

One-Match and All-Match Categories for Keywords Matching in Chatbot

Data Mining From Document-Append Nosql

Enhancement Algorithms for SQL-Based Chatbot

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