[PDF] Top 20 Improvements to the Bayesian Topic N Gram Models
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Improvements to the Bayesian Topic N Gram Models
... a Bayesian generative model for su- pervised language model ..., n-gram topic model has O(KV n ) parameters, which grow exponentially ... See full document
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Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
... For Bayesian LDA models, we can also explore the conjugacy of the Dirichlet-Multinomial prior- likelihood pairs to collapse out the Dirichlet vari- ables ...significant improvements on time ... See full document
9
Variational Inference in Nonconjugate Models
... Many models of interest, however, do not enjoy the properties required to take advantage of this easily derived ...nonconjugate models 1 include Bayesian logistic regression (Jaakkola and Jordan, ... See full document
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Bayesian Unsupervised Topic Segmentation
... Existing unsupervised cohesion-based approaches can be characterized in terms of the metric used to quantify cohesion and the search technique. Galley et al. (2003) characterize cohesion in terms of lexical chains – ... See full document
10
Grammatical Machine Translation
... translation models has not yet been attempted in dependency-based ...realization models that can easily be trained to reflect the ordering of the reference translations in the training corpus are ...such ... See full document
8
From n gram based to CRF based Translation Models
... language models have proven inconclusive, which might explain why our best results remain between one and two BLEU points behind the n-gram based system using comparable ...with improvements ... See full document
12
Perplexity on Reduced Corpora
... language models and topic models. In the case of language models, we often have to remove low-frequency words because of a lack of com- putational resources, since the feature space of k - ... See full document
10
N gram Counts and Language Models from the Common Crawl
... Finally, we investigate the relation between the amount of Common Crawl data used and improvements in MT qual- ity. To this end we train a system using Moses and standard settings but all available parallel data ... See full document
6
EXPLOITING N-GRAM IMPORTANCE AND ADDITIONAL KNOWEDGE BASED ON WIKIPEDIA FOR IMPROVEMENTS IN GAAC BASED DOCUMENT CLUSTERING
... We know that keyphrase extraction algorithms generally use such concepts, to extract terms which either have a good coverage of the document or are able to represent the document’s theme. Using these observations as the ... See full document
6
Bayesian Hidden Topic Markov Models
... with topic modeling; topic models offer a statistical model of textual ...Markov models is proposed using a fully Bayesian ...for topic modeling, its underlying assumptions ... See full document
120
Bayesian Checking for Topic Models
... We embed this discrepancy in a PPC and study it in several ways. First, we focus on topics that model their observations well; this helps separate interpretable topics from noisy topics (and “boiler- plate” topics, which ... See full document
11
Auto Sizing Neural Networks: With Applications to n gram Language Models
... neural n-gram language model (Bengio et ...learn models that are smaller than models trained without the method, while maintaining nearly the same ...and n-gram ... See full document
9
Analyzing Bayesian Crosslingual Transfer in Topic Models
... Crosslingual learning is an important area of nat- ural language processing that has driven appli- cations including text mining in multiple lan- guages (Ni et al., 2009; Smet and Moens, 2009), cultural difference ... See full document
15
N gram based Tense Models for Statistical Machine Translation
... After that, we propose to integrate such tense models into SMT systems in a dynamic way. It is well known there are many errors in the current MT output (David et al., 2006). Unlike previously making trouble with ... See full document
10
Statistical Representation of Grammaticality Judgements: the Limits of N Gram Models
... enriched n-gram models to track grammaticality judgements for different sorts of passive sentences in ...these models by specifying scoring functions to map the log probabilities (logprobs) of ... See full document
9
Language Identification of Short Text Segments with N-gram Models
... with n- gram language models. Because of the compact models that do not need word-based features, this approach is well suited for language identification tasks that have dozens of languages, ... See full document
8
Dependency Based N Gram Models for General Purpose Sentence Realisation
... phisticated n-gram models for sentence genera- tion from labelled bilexical dependencies, in the form of LFG ...The models include additional conditioning on parent GFs and differ- ent degrees ... See full document
8
N gram and Neural Language Models for Discriminating Similar Languages
... traditional n-gram model for this task, but only once the data set size is dramatically increased and given more time to experiment on the network parameters and ... See full document
8
Character n-Gram Embeddings to Improve RNN Language Models
... character n-grams based on research in the field of word embedding construction (Wieting et ...character n- gram embeddings and combines them with ordinary word em- ... See full document
9
LIMSI@WMT’16: Machine Translation of News
... estimate n-gram models that use large output vocabulary, thereby making the training of large neural net- work language models feasible both for target lan- guage models (Le et ... See full document
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