[PDF] Top 20 Less is More: Significance Based N gram Selection for Smaller, Better Language Models
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Less is More: Significance Based N gram Selection for Smaller, Better Language Models
... high-order N-gram language models esti- mated by standard techniques can be impractically ...high-order N-gram language models without dra- matically increasing ... See full document
10
N gram language models for massively parallel devices
... of N-gram language models is a computa- tional ...first language model de- signed for such hardware, using B-trees to maximize data parallelism and minimize memory footprint and ...CPU- ... See full document
10
Letter N Gram based Input Encoding for Continuous Space Language Models
... standard n-gram language models (Bengio et ...network language mod- els on millions of words resulting in a decrease of the word error rate for continuous speech recog- ...rescore ... See full document
10
Generalizing and Hybridizing Count based and Neural Language Models
... of n-gram components, non-linearities, or the connec- tion with neural network ...of n-gram LMs, which start with n-gram probabilities (Della Pietra et ...them based on ... See full document
10
An Empirical Comparison Between N gram and Syntactic Language Models for Word Ordering
... the N-gram model per- forms better on short sentences (less than 8 to- kens), and the syntactic model performs better on longer ...can better captured by the syntactic ...the ... See full document
10
Evaluation of Language Models over Croatian Newspaper Texts
... Statistical language modeling involves techniques and procedures that assign probabilities to word sequences or, said in other words, estimate the regularity of the ...statistical language models, ... See full document
34
Improvements to the Bayesian Topic N Gram Models
... is language model adapta- tion, which has been studied mainly in the area of speech ...trained models: an n-gram model p(w|h) and a topic model ...perform better than more ... See full document
11
From n gram based to CRF based Translation Models
... to better assess the strengths and weaknesses of our approach, we com- pare several test settings: the most favorable con- siders only one possible segmentation/reordering ˜ f for each f , obtained through forced ... See full document
12
Language Identification of Short Text Segments with N-gram Models
... an n-gram model grows rapidly with increasing n and training corpus ...exclude n-grams that occur fewer times than a given cut-off count. The n-grams that contribute only little to the ... See full document
8
N gram and Neural Language Models for Discriminating Similar Languages
... CSLTM. Based on the empirical study of (Zhang et al., 2015), character based ConvNets performed in line with traditional methods with data sets in the hundreds of thousands, and better with data sets ... See full document
8
Faster and Smaller N Gram Language Models
... conceptually based on tabular trie encodings wherein each n-gram key is stored as the concatena- tion of one word (here, the last) and an offset encod- ing the remaining words (here, the ... See full document
10
Book Reviews: Computational Approaches to Morphology and Syntax by Brian Roark and Richard Sproat
... the language engineer who wants to build an efficient, competitive ...a more up-to-date view of statistical parsing than offered by Manning and Sch ¨ utze (1999), with more coverage of advanced topics ... See full document
6
An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language
... We select three primary metrics with which to evaluate the various resulting corpora. The first is the distribution of sentence lengths. Sentence length is compared visually and through the sum of error as compared to ... See full document
5
Experience with a Stack Decoder Based HMM CSR and Back Off N Gram Language Models
... Experience with a Stack Decoder Based HMM CSR and Back Off N Gram Language Models E x p e r i e n c e w i t h a S t a c k D e c o d e r B a s e d H M M C S R a n d B a c k O f f N G r a m L a n g u a[.] ... See full document
5
Character n-Gram Embeddings to Improve RNN Language Models
... (RNN) language model that takes advantage of character ...character n-grams based on research in the field of word embedding construction (Wieting et ...character n- gram embeddings and ... See full document
9
A Joint Source Channel Model for Machine Transliteration
... called n-gram transliteration model (TM). With the n-gram TM model, we automate the orthographic alignment process to derive the aligned transliteration units from a bilingual ...The ... See full document
8
Native Language Identification: a Simple n gram Based Approach
... of more than 5% in the development ...has better results than the first experiment for classes Arabic and ...of n-grams of different POS ...seems better for identify- ing the native languages ... See full document
8
HIVEC: A Hierarchical Approach for Vector Representation Learning of Graphs
... In contrast to vocabulary generation methods discussed in [5], [6], we present a novel and reliable approach to generate the same for language modelling technique, which improves over previous methods. The main ... See full document
7
Language Models for Contextual Error Detection and Correction
... When it is known that a sequence is not valid in the language, this information can be used to decide which word from the confusible set should be selected. However, when the sequence simply has not been seen in ... See full document
8
Speech-Based Location Estimation of First Responders in a Simulated Search and Rescue Scenario
... FRs based on their speech commu- nications in a USAR ...perform better than the TF-IDF approach, but also being more robust to errors in highly imperfect automatic ...60% better than 0.015 WD ... See full document
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