Tag #n-gram
48 papers:
EDM-2019-DavisWY #education #graph #topic- N-gram Graphs for Topic Extraction in Educational Forums (GMD, CW, CY).
ICSME-2018-JimenezCTP #analysis #on the #parametricity- On the Impact of Tokenizer and Parameters on N-Gram Based Code Analysis (MJ, MC, YLT, MP), pp. 437–448.
KDD-2018-BaiOZFRST #query #scalability- Scalable Query N-Gram Embedding for Improving Matching and Relevance in Sponsored Search (XB0, EO, YZ, AF, AR, RS, AT), pp. 52–61.
- ICSME-2017-TerdchanakulHPM #classification #debugging #using
- Bug or Not? Bug Report Classification Using N-Gram IDF (PT, HH, PP, KM), pp. 534–538.
ASE-2016-WangCMT #debugging #detection #modelling #named- Bugram: bug detection with n-gram language models (SW, DC, DMA, LT), pp. 708–719.
- KDD-2015-GalleT #documentation
- Reconstructing Textual Documents from n-grams (MG, MT), pp. 329–338.
ESEC-FSE-2015-SaraivaBZ #developer #how- Products, developers, and milestones: how should I build my N-Gram language model (JS, CB, TZ), pp. 998–1001.
ICPC-2014-SuzukiSIH #approach #modelling #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 #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 #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 #testing- Interpolated n-grams for model based testing (PT, RT, DCN), pp. 562–572.
ICDAR-2013-RoySSJ #documentation #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 #performance #sequence- Efficient and Effective KNN Sequence Search with Approximate n-grams (XW, XD, AKHT, ZZ), pp. 1–12.
ECIR-2013-JameelL #documentation #topic- An N-Gram Topic Model for Time-Stamped Documents (SJ, WL), pp. 292–304.
MLDM-2013-PohlZ #automation #recognition #speech #using- Using Part of Speech N-Grams for Improving Automatic Speech Recognition of Polish (AP, BZ), pp. 492–504.
CIKM-2012-QureshiOP #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
- Character n-Gram Spotting in Document Images (MSP, KPS, CVJ), pp. 941–945.
- CIKM-2011-BespalovBQS #analysis #classification #sentiment
- Sentiment classification based on supervised latent n-gram analysis (DB, BB, YQ, AS), pp. 375–382.
ICPR-2010-KojimaMK #recognition- Object Recognition Based on n-gram Expression of Human Actions (AK, HM, KK), pp. 372–375.
- KDIR-2010-KumarVSV #clustering #documentation #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 #named- Agro-Gator: digesting experts, logs, and N-grams (MH), p. 706.
- CIKM-2009-MouzaLRS #algebra #named #string #using
- AS-index: a structure for string search using n-grams and algebraic signatures (CdM, WL, PR, TJES), pp. 295–304.
- ECIR-2009-Barron-CedenoR #automation #comparison #detection #on the
- On Automatic Plagiarism Detection Based on n-Grams Comparison (ABC, PR), pp. 696–700.
- ECIR-2009-KleinN #correlation #documentation
- Correlation of Term Count and Document Frequency for Google N-Grams (MK, MLN), pp. 620–627.
- ECIR-2009-ParaparFB #modelling #retrieval #scalability
- Revisiting N-Gram Based Models for Retrieval in Degraded Large Collections (JP, AF, AB), pp. 680–684.
DocEng-2008-KumarS #automation #documentation #using- Automatic keyphrase extraction from scientific documents using N-gram filtration technique (NK, KS), pp. 199–208.
- CIKM-2008-Coetzee #named
- TinyLex: static n-gram index pruning with perfect recall (DC), pp. 409–418.
- KDD-2008-IfrimBW #categorisation #performance
- Fast logistic regression for text categorization with variable-length n-grams (GI, GHB, GW), pp. 354–362.
- VLDB-2005-KimWLL #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 #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 #using #web- Mining web content outliers using structure oriented weighting techniques and N-grams (MA, KB, RA), pp. 482–487.
- SAC-2005-MylesC
- K-gram based software birthmarks (GM, CSC), pp. 314–318.
- ICDAR-2003-El-NasanVN #recognition #using
- Handwriting Recognition Using Position Sensitive Letter N-Gram Matching (AEN, SV, GN), p. 577–?.
- ICDAR-2003-PerraudVML #modelling #recognition
- N-Gram and N-Class Models for On line Handwriting Recognition (FP, CVG, EM, PML), p. 1053–?.
- ECIR-2003-PengS #classification #modelling #naive bayes
- Combining Naive Bayes and n-Gram Language Models for Text Classification (FP, DS), pp. 335–350.
- SIGIR-2003-MayfieldM
- Single n-gram stemming (JM, PM), pp. 415–416.
- ICDAR-2001-BrakensiekR #comparison #recognition
- A Comparison of Character N-Grams and Dictionaries Used for Script Recognition (AB, GR), pp. 241–245.
- ICPR-v2-2000-Fernau
- k-gram Extensions of Terminal Distinguishable Languages (HF), pp. 2125–2128.
- ICPR-v4-2000-BrakensiekWR #documentation #hybrid #modelling #recognition
- Improved Degraded Document Recognition with Hybrid Modeling Techniques and Character N-Grams (AB, DW, GR), pp. 4438–4441.
- SIGIR-2000-Ogawa #documentation #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 #optimisation #query
- Optimizing Query Evaluation in n-gram Indexing (YO, TM), pp. 367–368.
ECDL-1997-HardingCW #probability #retrieval #using- Probabilistic Retrieval of OCR Degraded Text Using N-Grams (SMH, WBC, CW), pp. 345–359.
- ICDAR-1997-BruggerZI #documentation #modelling #recognition #using
- Modeling Documents for Structure Recognition Using Generalized N-Grams (RB, AWZ, RI), pp. 56–60.
- ICDAR-1997-LankB #documentation #information management #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 #retrieval #using
- Using n-Grams for Korean Text Retrieval (JHL, JSA), pp. 216–224.
- ICDAR-v1-1995-MoriAM #documentation #recognition
- Japanese document recognition based on interpolated n-gram model of character (HM, HA, SM), pp. 274–277.
- ICDAR-1993-Pflug #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
- Generating a Dynamic Hypertext Environment with n-gram Analysis (CP, CKN), pp. 148–153.
