Machine Learning Approach to Search Query Classification

2011 ◽  
pp. 329-344
Author(s):  
Isak Taksa ◽  
Sarah Zelikovitz ◽  
Amanda Spink
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Hierarchical Classification ◽  
Search Query ◽  
Short Text ◽  
Search Queries ◽  
Tuning Parameters ◽  
Commercial Search Engine ◽  
Query Classification

Search query classification is a necessary step for a number of information retrieval tasks. This chapter presents an approach to non-hierarchical classification of search queries that focuses on two specific areas of machine learning: short text classification and limited manual labeling. Typically, search queries are short, display little class specific information per single query and are therefore a weak source for traditional machine learning. To improve the effectiveness of the classification process the chapter introduces background knowledge discovery by using information retrieval techniques. The proposed approach is applied to a task of age classification of a corpus of queries from a commercial search engine. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters.

Machine Learning Approach to Search Query Classification

2012 ◽  
pp. 467-482
Author(s):  
Isak Taksa ◽  
Sarah Zelikovitz ◽  
Amanda Spink
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Hierarchical Classification ◽  
Search Query ◽  
Short Text ◽  
Search Queries ◽  
Tuning Parameters ◽  
Commercial Search Engine ◽  
Query Classification

Search query classification is a necessary step for a number of information retrieval tasks. This chapter presents an approach to non-hierarchical classification of search queries that focuses on two specific areas of machine learning: short text classification and limited manual labeling. Typically, search queries are short, display little class specific information per single query and are therefore a weak source for traditional machine learning. To improve the effectiveness of the classification process the chapter introduces background knowledge discovery by using information retrieval techniques. The proposed approach is applied to a task of age classification of a corpus of queries from a commercial search engine. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters.

Non-Topical Classification of Query Logs Using Background Knowledge

2011 ◽  
pp. 194-212
Author(s):  
Isak Taksa ◽  
Sarah Zelikovitz ◽  
Amanda Spink
Keyword(s):  
Machine Learning ◽  
Text Categorization ◽  
Critical Role ◽  
Hierarchical Classification ◽  
Background Knowledge ◽  
Machine Learning Algorithms ◽  
Short Text ◽  
Tuning Parameters ◽  
Query Logs

Background knowledge has been actively investigated as a possible means to improve performance of machine learning algorithms. Research has shown that background knowledge plays an especially critical role in three atypical text categorization tasks: short-text classification, limited labeled data, and non-topical classification. This chapter explores the use of machine learning for non-hierarchical classification of search queries, and presents an approach to background knowledge discovery by using information retrieval techniques. Two different sets of background knowledge that were obtained from the World Wide Web, one in 2006 and one in 2009, are used with the proposed approach to classify a commercial corpus of web query data by the age of the user. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters, and exploring impact of the dynamic web on classification results.

Non-Topical Classification of Query Logs Using Background Knowledge

2012 ◽  
pp. 598-615
Author(s):  
Isak Taksa ◽  
Sarah Zelikovitz ◽  
Amanda Spink
Keyword(s):  
Machine Learning ◽  
Text Categorization ◽  
Critical Role ◽  
Hierarchical Classification ◽  
Background Knowledge ◽  
Machine Learning Algorithms ◽  
Short Text ◽  
Tuning Parameters ◽  
Query Logs

Background knowledge has been actively investigated as a possible means to improve performance of machine learning algorithms. Research has shown that background knowledge plays an especially critical role in three atypical text categorization tasks: short-text classification, limited labeled data, and non-topical classification. This chapter explores the use of machine learning for non-hierarchical classification of search queries, and presents an approach to background knowledge discovery by using information retrieval techniques. Two different sets of background knowledge that were obtained from the World Wide Web, one in 2006 and one in 2009, are used with the proposed approach to classify a commercial corpus of web query data by the age of the user. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters, and exploring impact of the dynamic web on classification results.

scholarly journals Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06257
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Rubiel Vargas-Cañas ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Music Information Retrieval ◽  
Machine Learning Classification ◽  
Information Retrieval Methods ◽  
Music Information

scholarly journals Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease

2019 ◽  
Vol 23 ◽  
pp. 101811 ◽  
Author(s):  
Jun Pyo Kim ◽  
Jeonghun Kim ◽  
Yu Hyun Park ◽  
Seong Beom Park ◽  
Jin San Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Hierarchical Classification

scholarly journals Research on Sentiment Classification of Online Travel Review Text

2020 ◽  
Vol 10 (15) ◽  
pp. 5275 ◽  
Author(s):  
Wen Chen ◽  
Zhiyun Xu ◽  
Xiaoyao Zheng ◽  
Qingying Yu ◽  
Yonglong Luo
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Expansion Method ◽  
Sentiment Classification ◽  
Classification Model ◽  
Knowledge Graph ◽  
Short Text ◽  
Online Travel ◽  
Online Travel Review

In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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