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Understanding Temporal Intent of User Query Based on Time-Based Query Classification
[chapter]
2013
Communications in Computer and Information Science
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In this paper, we propose a timebased query classification approach to understand user's temporal intent automatically. ...
Finally, for a new given query, we propose a machine learning method to decide its class in terms of its search frequency over time recorded in Web query logs. ...
Kira et al. explored how to use time series technique to model and predict user behavior over time including trends, periodicities and surprises [13] . ...
doi:10.1007/978-3-642-41644-6_31
fatcat:26lhz26rurgvxkdcvhyorfakdq
Normalized Web Distance Based Web Query Classification
2012
Journal of Computer Science
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Problem statement: The problem is to classify a given web query to a set of 67 target categories. The target categories are ranked based on the degree of similarity to a given query. ...
The categories are then ranked based on three parameters of the intermediate categories namely, position, frequency and a combination of frequency and position. ...
While query logs help to mine the trends at a point of time and the characteristics of the search engine user and the query, they are also extensively used in the prediction of the future need of the user ...
doi:10.3844/jcssp.2012.804.808
fatcat:zyihwqsycrggbbbmggs2rjovfm
Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification
[chapter]
2009
Lecture Notes in Computer Science
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In this work, the active learning is used to query a label for an unlabeled data on top of a semisupervised classifier. This work focuses on the query selection criterion. ...
Experimental results show the effectiveness of the proposed query selection criterion in comparison to existing techniques. ...
Therefore, labeling more instances guarantee to improve the predictive performance of the classification. ...
doi:10.1007/978-3-642-05224-8_22
fatcat:23edeptytfcsjkfhvpq24sat3e
Automatic classification of Web queries using very large unlabeled query logs
2007
ACM Transactions on Information Systems
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Such classification becomes critical if the system must route queries to a subset of topic-specific and resource-constrained back-end databases. ...
and a novel approach based on mining selectional preferences from a large unlabeled query log. ...
topic-based classification. ...
doi:10.1145/1229179.1229183
fatcat:atp5ern5tjdotebis4fgegpvw4
Textual Query Based Image Retrieval
2015
International Journal on Recent and Innovation Trends in Computing and Communication
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Our system generates the inverted file to automatically find the positive Web images that are related to the textual query as well as the negative Web images that are irrelevant to the textual query. ...
Real-time textual query-based personal photo retrieval system by leveraging millions of Web images and their associated rich textual descriptions. Then user provides a textual query. ...
TEXTUAL QUERY-BASED CONSUMER PHOTO RETRIEVAL SYSTEM A. k-Nearest neighbor Image classification approach, derived from the kNN classification strategy, which is particularly suited to be used when classifying ...
doi:10.17762/ijritcc2321-8169.150322
fatcat:hqohttalvnb77kzqb6ps7lp32e
Learning-Based SPARQL Query Performance Prediction
[chapter]
2016
Lecture Notes in Computer Science
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According to the predictive results of query performance, queries can be rewritten to reduce time cost or rescheduled to the time when the resource is not in contention. ...
In this paper, we focus on representing SPARQL queries with feature vectors and using these feature vectors to train predictive models that are used to predict the performance of SPARQL queries. ...
Compare to one-step prediction, the two-step prediction has labeling before predictive model training and classification step before prediction. ...
doi:10.1007/978-3-319-48740-3_23
fatcat:2bg3ujtmencnfppi3o4js7mipi
Scalable Semi-Supervised Query Classification Using Matrix Sketching
2016
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
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Using matrix sketching, we significantly improve the user intent classification accuracy by leveraging large amounts of unlabeled queries. ...
While our labeled data is quite limited, we have access to virtually an unlimited amount of unlabeled queries, which could be used to induce useful representations: for instance by principal component ...
In experiments, we significantly improve the intent classification accuracy by learning sentence representations from huge amounts of unlabeled sentences, outperforming a strong baseline based on word ...
doi:10.18653/v1/p16-2002
dblp:conf/acl/KimSS16
fatcat:prj2akqndbgwvcoe76soik3qri
Automatic web query classification using labeled and unlabeled training data
2005
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05
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We examine three approaches to topical categorization of general web queries: matching against a list of manually labeled queries, supervised learning of classifiers, and mining of selectional preference ...
Each approach has its advantages in tackling the web query classification recall problem, and combining the three techniques allows us to classify a substantially larger proportion of queries than any ...
PRIOR WORK Initial efforts in automatic web query classification have focused mostly on task-based classification and unsupervised clustering. ...
doi:10.1145/1076034.1076138
dblp:conf/sigir/BeitzelJFGLCK05
fatcat:2mrw3evqondy7pmoop7dnkq2tu
Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow
[article]
2021
arXiv
pre-print
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As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. ...
Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. ...
ACKNOWLEDGEMENT The authors would like to thank Stack Exchange Inc. for sharing the dataset, and the anonymous reviewers for their insightful comments and suggestions. ...
arXiv:2102.00826v2
fatcat:5wurhi5usnaqbftgo26vj45svq
Classification-based resource selection
2009
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09
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Resource selection refers to the subtask of deciding, given a query, which collections to search. Most existing resource selection methods rely on evidence found in collection content. ...
We derive evidence from three different sources: collection documents, the topic of the query, and query click-through data. ...
As described in Section 4, we train a classification system to predict the inclusion of a collection in the merged results based on its impact on a full-dataset retrieval. ...
doi:10.1145/1645953.1646115
dblp:conf/cikm/ArguelloCD09
fatcat:u67z4iymajejhadslxhw5wxofu
Attention-Based Query Expansion Learning
[article]
2020
arXiv
pre-print
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In this paper we propose a more principled framework to query expansion, where one trains, in a discriminative manner, a model that learns how images should be aggregated to form the expanded query. ...
Interestingly, despite the undeniable empirical success of query expansion, ad-hoc methods with different caveats have dominated the landscape, and not a lot of research has been done on learning how to ...
This enables us to have an auxiliary linear classifier that predicts whetherd i is relevant to the query or not. ...
arXiv:2007.08019v1
fatcat:6l2wlne66faw5gbltax2cagnlm
Malicious domain detection based on DNS query using Machine Learning
2020
International Journal of Emerging Trends in Engineering Research
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In this paper, we propose a method of detecting malicious domains based on the connection behavior analysis technique using machine learning algorithms. ...
Besides, in order to classify the normal domain and malicious domain, we select Random Forest (RF) supervised learning algorithms. ...
According to the research of the trend of network attacks in 2020 [6] , the techniques of attacks on users by spreading malicious domains are predicted to have sophisticated transformations and serious ...
doi:10.30534/ijeter/2020/53852020
fatcat:thoph2pxfvgs3ok63jlnk62fwe
Structured Query-Based Image Retrieval Using Scene Graphs
[article]
2020
arXiv
pre-print
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Notably, we are able to achieve high recall even on low to medium frequency objects found in the long-tailed COCO-Stuff dataset, and find that adding a visual relationship-inspired loss boosts our recall ...
We examine how visual relationships, derived from scene graphs, can be used as structured queries. ...
This follows the trend seen in the baseline of subject and object-only queries outperforming the predicate-based queries. ...
arXiv:2005.06653v1
fatcat:uhfmzpvupbewxlig2tk62tj5r4
Learning-based SPARQL query performance modeling and prediction
2017
World wide web (Bussum)
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Further, the effort exploiting machine learning techniques is limited. In this paper, we adopt machine learning techniques to predict the performance of SPARQL queries. ...
Our work focuses on modelling features of a SPARQL query to a vector representation and use these feature vectors to train predictive models. ...
Although it is arguably less accurate than machine learning based prediction, it is faster than machine learning prediction. ...
doi:10.1007/s11280-017-0498-1
fatcat:2mmbwvizwrgctcsg723pf5feeq
Intent-Based User Segmentation with Query Enhancement
2013
International Journal of Information Retrieval Research
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The web page is assigned to nodes in the hierarchy for processing learning and predicting interests. ...
For example, a model could be learned based on search queries of a group of people who bought tablet PCs online to segment users for tablet PCs ads delivery. ...
doi:10.4018/ijirr.2013100101
fatcat:2sh4kotdsrh33k4etfu2o3xuoq
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