Stem gram$ (all stems)

58 papers:

KDD-2015-GalleT #documentation #n-gram

Reconstructing Textual Documents from n-grams (MG, MT), pp. 329–338.

ESEC-FSE-2015-SaraivaBZ #developer #how #n-gram

Products, developers, and milestones: how should I build my N-Gram language model (JS, CB, TZ), pp. 998–1001.

DocEng-2014-DavisonMM #named #privacy #semantics #similarity

P-GTM: privacy-preserving google tri-gram method for semantic text similarity (OD, AM, EEM), pp. 81–84.

ICPC-2014-SuzukiSIH #approach #modelling #n-gram #using

An approach for evaluating and suggesting method names using n-gram models (TS, KS, FI, SH), pp. 271–274.

KDIR-2014-AbdallahI #classification #modelling #n-gram #using #web

URL-based Web Page Classification — A New Method for URL-based Web Page Classification Using n-Gram Language Models (TAA, BdlI), pp. 14–21.

KDIR-2014-LiuF14a #analysis #classification #n-gram #sentiment #similarity #web

Combining N-gram based Similarity Analysis with Sentiment Analysis in Web Content Classification (SL, TF), pp. 530–537.

ICSE-2014-TonellaTN #modelling #n-gram #testing

Interpolated n-grams for model based testing (PT, RT, DCN), pp. 562–572.

ICDAR-2013-RoySSJ #documentation #n-gram #segmentation #using

Character N-Gram Spotting on Handwritten Documents Using Weakly-Supervised Segmentation (UR, NS, KPS, CVJ), pp. 577–581.

VLDB-2014-WangDTZ13 #approximate #effectiveness #n-gram #performance #sequence

Efficient and Effective KNN Sequence Search with Approximate n-grams (XW, XD, AKHT, ZZ), pp. 1–12.

ECIR-2013-JameelL #documentation #n-gram #topic

An N-Gram Topic Model for Time-Stamped Documents (SJ, WL), pp. 292–304.

MLDM-2013-PohlZ #automation #n-gram #recognition #speech #using

Using Part of Speech N-Grams for Improving Automatic Speech Recognition of Polish (AP, BZ), pp. 492–504.

CIKM-2012-BonomiXCF #privacy

Frequent grams based embedding for privacy preserving record linkage (LB, LX, RC, BCMF), pp. 1597–1601.

CIKM-2012-QureshiOP #n-gram #using #wiki

Short-text domain specific key terms/phrases extraction using an n-gram model with wikipedia (MAQ, CO, GP), pp. 2515–2518.

ICDAR-2011-PraveenSJ #documentation #image #n-gram

Character n-Gram Spotting in Document Images (MSP, KPS, CVJ), pp. 941–945.

CIKM-2011-BespalovBQS #analysis #classification #n-gram #sentiment

Sentiment classification based on supervised latent n-gram analysis (DB, BB, YQ, AS), pp. 375–382.

ECIR-2010-KabadjovSSPP #evaluation #summary #taxonomy #using

Enhancing N-Gram-Based Summary Evaluation Using Information Content and a Taxonomy (MAK, JS, RS, MP, BP), pp. 662–666.

ICPR-2010-KojimaMK #n-gram #recognition

Object Recognition Based on n-gram Expression of Human Actions (AK, HM, KK), pp. 372–375.

KDIR-2010-KumarVSV #clustering #documentation #n-gram #wiki

Exploiting N-gram Importance and Wikipedia based Additional Knowledge for Improvements in GAAC based Document Clustering (NK, VVBV, KS, VV), pp. 182–187.

SIGIR-2010-Huggett #n-gram #named

Agro-Gator: digesting experts, logs, and N-grams (MH), p. 706.

ICDAR-2009-SchullerSRK #analysis #bibliography #estimation #online #quote

“The Godfather” vs. “Chaos”: Comparing Linguistic Analysis Based on On-line Knowledge Sources and Bags-of-N-Grams for Movie Review Valence Estimation (BWS, JS, GR, TK), pp. 858–862.

CIKM-2009-IslamI #set #using #web

Real-word spelling correction using Google web 1Tn-gram data set (AI, DI), pp. 1689–1692.

CIKM-2009-MouzaLRS #algebra #n-gram #named #string #using

AS-index: a structure for string search using n-grams and algebraic signatures (CdM, WL, PR, TJES), pp. 295–304.

CIKM-2009-TangSB #string

Space-economical partial gram indices for exact substring matching (NT, LS, PAB), pp. 285–294.

ECIR-2009-Barron-CedenoR #automation #comparison #detection #n-gram #on the

On Automatic Plagiarism Detection Based on n-Grams Comparison (ABC, PR), pp. 696–700.

ECIR-2009-KleinN #correlation #documentation #n-gram

Correlation of Term Count and Document Frequency for Google N-Grams (MK, MLN), pp. 620–627.

ECIR-2009-ParaparB #algorithm #clustering #documentation #evaluation

Evaluation of Text Clustering Algorithms with N-Gram-Based Document Fingerprints (JP, AB), pp. 645–653.

ECIR-2009-ParaparFB #modelling #n-gram #retrieval #scalability

Revisiting N-Gram Based Models for Retrieval in Degraded Large Collections (JP, AF, AB), pp. 680–684.

DocEng-2008-KumarS #automation #documentation #n-gram #using

Automatic keyphrase extraction from scientific documents using N-gram filtration technique (NK, KS), pp. 199–208.

SIGMOD-2008-YangWL #approximate #cost analysis #query #string

Cost-based variable-length-gram selection for string collections to support approximate queries efficiently (XY, BW, CL), pp. 353–364.

CIKM-2008-Coetzee #n-gram #named

TinyLex: static n-gram index pruning with perfect recall (DC), pp. 409–418.

ICPR-2008-KazuiMMF #detection #matrix #using

Incoherent motion detection using a time-series Gram matrix feature (MK, MM, SM, HF), pp. 1–5.

KDD-2008-IfrimBW #categorisation #n-gram #performance

Fast logistic regression for text categorization with variable-length n-grams (GI, GHB, GW), pp. 354–362.

VLDB-2007-LeeNS #distance #edit distance #string

Extending Q-Grams to Estimate Selectivity of String Matching with Low Edit Distance (HL, RTN, KS), pp. 195–206.

VLDB-2007-LiWY #approximate #named #performance #query #string #using

VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams (CL, BW, XY), pp. 303–314.

VLDB-2005-AugstenBG #approximate #using

Approximate Matching of Hierarchical Data Using pq-Grams (NA, MHB, JG), pp. 301–312.

VLDB-2005-KimWLL #n-gram #named #performance

n-Gram/2L: A Space and Time Efficient Two-Level n-Gram Inverted Index Structure (MSK, KYW, JGL, MJL), pp. 325–336.

CIKM-2005-MiaoKM #clustering #comparative #documentation #evaluation #n-gram #using

Document clustering using character N-grams: a comparative evaluation with term-based and word-based clustering (YM, VK, EEM), pp. 357–358.

SAC-2005-AgyemangBA #mining #n-gram #using #web

Mining web content outliers using structure oriented weighting techniques and N-grams (MA, KB, RA), pp. 482–487.

SAC-2005-MylesC #n-gram

K-gram based software birthmarks (GM, CSC), pp. 314–318.

ICPR-v2-2004-ZhengZZ #realtime #recognition #using

Real-Time Face Recognition Using Gram-Schmidt Orthogonalization for LDA (WZ, CZ, LZ), pp. 403–406.

ICDAR-2003-El-NasanVN #n-gram #recognition #using

Handwriting Recognition Using Position Sensitive Letter N-Gram Matching (AEN, SV, GN), p. 577–?.

ICDAR-2003-PerraudVML #modelling #n-gram #recognition

N-Gram and N-Class Models for On line Handwriting Recognition (FP, CVG, EM, PML), p. 1053–?.

ECIR-2003-PengS #classification #modelling #n-gram #naive bayes

Combining Naive Bayes and n-Gram Language Models for Text Classification (FP, DS), pp. 335–350.

SIGIR-2003-MayfieldM #n-gram

Single n-gram stemming (JM, PM), pp. 415–416.

HPDC-2002-LaszewskiGPF #execution #grid #named #query

InfoGram: A Grid Service that Supports Both Information Queries and Job Execution (GvL, JG, CJP, ITF), pp. 333–342.

ICDAR-2001-BrakensiekR #comparison #n-gram #recognition

A Comparison of Character N-Grams and Dictionaries Used for Script Recognition (AB, GR), pp. 241–245.

ICDAR-2001-HasegawaAM #algorithm #online #recognition

A Bayesian Bi-gram Scheme for HMM Online Handwriting Recognition Algorithm (TH, KA, TM), pp. 1012–1016.

ICPR-v2-2000-Fernau #n-gram

k-gram Extensions of Terminal Distinguishable Languages (HF), pp. 2125–2128.

ICPR-v4-2000-BrakensiekWR #documentation #hybrid #modelling #n-gram #recognition

Improved Degraded Document Recognition with Hybrid Modeling Techniques and Character N-Grams (AB, DW, GR), pp. 4438–4441.

SIGIR-2000-Ogawa #documentation #n-gram #performance #pseudo #ranking #retrieval

Pseudo-frequency method: an efficient document ranking retrieval method for n-gram indexing (YO), pp. 321–323.

SIGIR-1998-OgawaM #evaluation #n-gram #optimisation #query

Optimizing Query Evaluation in n-gram Indexing (YO, TM), pp. 367–368.

ICDAR-1997-BruggerZI #documentation #modelling #n-gram #recognition #using

Modeling Documents for Structure Recognition Using Generalized N-Grams (RB, AWZ, RI), pp. 56–60.

ICDAR-1997-LankB #documentation #information management #n-gram #named #recognition #representation #visual notation

N-grams: a well-structured knowledge representation for recognition of graphical documents (EL, DB), pp. 801–804.

SIGIR-1996-LeeA #n-gram #retrieval #using

Using n-Grams for Korean Text Retrieval (JHL, JSA), pp. 216–224.

ICDAR-v1-1995-MoriAM #documentation #n-gram #recognition

Japanese document recognition based on interpolated n-gram model of character (HM, HA, SM), pp. 274–277.

ICDAR-1993-Pflug #n-gram #recognition #set #using

Using n-grams for the definition of a training set for cursive handwriting recognition (VP), pp. 295–298.

CIKM-1993-PearceN #analysis #generative #hypermedia #n-gram

Generating a Dynamic Hypertext Environment with n-gram Analysis (CP, CKN), pp. 148–153.

HT-ECHT-1992-AmannS #graph #named #query

Gram: A Graph Data Model and Query Language (BA, MS), pp. 201–211.