47,759 Hits in 6.6 sec

Understanding Temporal Intent of User Query Based on Time-Based Query Classification [chapter]

Pengjie Ren, Zhumin Chen, Xiaomeng Song, Bin Li, Haopeng Yang, Jun Ma
2013 Communications in Computer and Information Science  
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  
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]

Krikamol Muandet, Sanparith Marukatat, Cholwich Nattee
2009 Lecture Notes in Computer Science  
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

Steven M. Beitzel, Eric C. Jensen, David D. Lewis, Abdur Chowdhury, Ophir Frieder
2007 ACM Transactions on Information Systems  
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

Patil Patil
2015 International Journal on Recent and Innovation Trends in Computing and Communication  
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]

Wei Emma Zhang, Quan Z. Sheng, Kerry Taylor, Yongrui Qin, Lina Yao
2016 Lecture Notes in Computer Science  
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

Young-Bum Kim, Karl Stratos, Ruhi Sarikaya
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
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

Steven M. Beitzel, Eric C. Jensen, Ophir Frieder, David Grossman, David D. Lewis, Abdur Chowdhury, Aleksandr Kolcz
2005 Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05  
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]

Kaibo Cao
2021 arXiv   pre-print
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

Jaime Arguello, Jamie Callan, Fernando Diaz
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
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]

Albert Gordo and Filip Radenovic and Tamara Berg
2020 arXiv   pre-print
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

Cho Do Xuan
2020 International Journal of Emerging Trends in Engineering Research  
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]

Brigit Schroeder, Subarna Tripathi
2020 arXiv   pre-print
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

Wei Emma Zhang, Quan Z. Sheng, Yongrui Qin, Kerry Taylor, Lina Yao
2017 World wide web (Bussum)  
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

Wei Xiong, Michael Recce, Brook Wu
2013 International Journal of Information Retrieval Research  
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
« Previous Showing results 1 — 15 out of 47,759 results