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Bigram
Known as:
Skipping bigrams
, Skipping bigram
, Bigram frequency attack
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A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words. A bigram is an n…
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Related topics
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22 relations
Automatic summarization
Banburismus
Cipher Department of the High Command of the Wehrmacht
Collocation
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Broader (1)
Natural language processing
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2015
Highly Cited
2015
Neural Machine Translation of Rare Words with Subword Units
Rico Sennrich
,
B. Haddow
,
Alexandra Birch
Annual Meeting of the Association for…
2015
Corpus ID: 1114678
Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem…
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Review
2014
Review
2014
The Tangled Web of Password Reuse
Anupam Das
,
Joseph Bonneau
,
M. Caesar
,
N. Borisov
,
Xiaofeng Wang
Network and Distributed System Security Symposium
2014
Corpus ID: 17528191
Today's Internet services rely heavily on text-based passwords for user authentication. The pervasiveness of these services…
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Highly Cited
2012
Highly Cited
2012
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
Sida I. Wang
,
Christopher D. Manning
Annual Meeting of the Association for…
2012
Corpus ID: 217537
Variants of Naive Bayes (NB) and Support Vector Machines (SVM) are often used as baseline methods for text classification, but…
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Highly Cited
2012
Highly Cited
2012
Detecting Hate Speech on the World Wide Web
William Warner
,
Julia Hirschberg
2012
Corpus ID: 12477446
We present an approach to detecting hate speech in online text, where hate speech is defined as abusive speech targeting specific…
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Highly Cited
2010
Highly Cited
2010
All the News That's Fit to Reprint: Do Investors React to Stale Information?
Paul C. Tetlock
2010
Corpus ID: 17419856
This article tests whether stock market investors appropriately distinguish between new and old information about firms. I define…
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Highly Cited
2008
Highly Cited
2008
Simple Semi-supervised Dependency Parsing
Terry Koo
,
X. Carreras
,
M. Collins
Annual Meeting of the Association for…
2008
Corpus ID: 1916754
We present a simple and effective semisupervised method for training dependency parsers. We focus on the problem of lexical…
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Highly Cited
2004
Highly Cited
2004
Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics
Chin-Yew Lin
,
F. Och
Annual Meeting of the Association for…
2004
Corpus ID: 1586456
In this paper we describe two new objective automatic evaluation methods for machine translation. The first method is based on…
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Highly Cited
1996
Highly Cited
1996
A New Statistical Parser Based on Bigram Lexical Dependencies
M. Collins
Annual Meeting of the Association for…
1996
Corpus ID: 12615602
This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse…
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Highly Cited
1994
Highly Cited
1994
Tree-based state tying for high accuracy acoustic modelling
S. Young
,
J. Odell
,
P. Woodland
1994
Corpus ID: 16667309
The key problem to be faced when building a HMM-based continuous speech recogniser is maintaining the balance between model…
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Highly Cited
1990
Highly Cited
1990
Applications of stochastic context-free grammars using the Inside-Outside algorithm
K. Lari
,
S. Young
1990
Corpus ID: 53736294
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