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An Efficient Indexer for Large N Gram Corpora
... be avoided depending on the application needs. Our program requires about one day of offline processing due to resorting the entire data a few times. Note that some of the files in the corpus need to be sorted as many as ... See full document
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Unsupervised Multiword Segmentation of Large Corpora using Prediction Driven Decomposition of n grams
... each n-gram over- lap in the sentence, taking any previous breaks as given while considering only the minimum breaks necessary to resolve any overlaps that directly influence the segmentation of the two ... See full document
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Building Large Corpora from the Web Using a New Efficient Tool Chain
... a large num- ber of hosts which never occur in search engine re- sults can be discovered through long-term ...how large a proportion r of the total URLs comes from to the n most popu- lar hosts in ... See full document
8
TMU Transformer System Using BERT for Re ranking at BEA 2019 Grammatical Error Correction on Restricted Track
... on large-scale corpora contributes to the improved hypotheses of the GEC model (Chollampatt and Ng, ...from large-scale raw data on learner corpora to explic- itly take into account ... See full document
6
Manipulating Large Corpora for Text Classification
... a large collection of data and propose a method for text classifi- cation which manipulates data using two well-known machine learning techniques, Naive Bayes(NB) and Support Vector Ma- ...more efficient. ... See full document
8
Bootstrapping Large Sense Tagged Corpora
... The work presented in this paper relates to work pre- viously reported in (Yarowsky, 1995), where few tagged seeds are used to train a decision list, which is then em- ployed to tag new unlabeled instances. An ... See full document
5
Combining String and Context Similarity for Bilingual Term Alignment from Comparable Corpora
... target n-grams, there are p×q second order ...a large number of features needs to be considered to achieve robust ...solve large-scale, linear classifications prob- ... See full document
12
Automatic Construction of Large Readability Corpora
... using corpora manually annotated with readability classifications to train automatic learning models, based on a large set of text metrics, including deeper features, for example derived from ... See full document
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... Reversible Grammar in NLP The Balancing Act Computational Phonology Third Workshop on Very Large Corpora Fourth Workshop on Very Large Corpora Empirical Methods in NLP Fifth Workshop on [r] ... See full document
9
An Unsupervised Query Rewriting Approach Using N gram Co occurrence Statistics to Find Similar Phrases in Large Text Corpora
... It is unclear how well these two latter ap- proaches potentially scale beyond bigrams or tri- grams. Further, they assume that the length of the input/output phrases is known in advance. How- ever, the task that we are ... See full document
9
Efficient, Compositional, Order sensitive n gram Embeddings
... We compare similarities between source and target phrases extracted from the paraphrase database (PPDB). To create our evaluation set of source and a pair of corresponding target phrases, we randomly sampled source ... See full document
6
Using Large Corpus N gram Statistics to Improve Recurrent Neural Language Models
... We experiment on a medium-size (2 layers with 650 hidden states) LSTM language model (Zaremba et al., 2014) over two corpora: Wiki- text (Merity et al., 2016) and Google Billion-Word (Chelba et al., 2013) (1B). We ... See full document
6
A Dynamic Programming Algorithm for Computing N gram Posteriors from Lattices
... of n-gram posterior probabilities from lattices has applications in lattice-based minimum Bayes-risk de- coding in statistical machine translation and the estimation of expected document frequencies from ... See full document
10
Reduced n gram Models for English and Chinese Corpora
... A distortion in the use of phrase frequencies had been observed in the small railway timetable Vodis Corpus when the bigram “RAIL ENQUIRIES” and its super-phrase “BRITISH RAIL ENQUIRIES” were examined. Both occur 73 ... See full document
7
Scaling Distributional Similarity to Large Corpora
... Having generated our d length bit signatures for each of our n terms, we take these signatures and randomly permute the bits. Each vector has the same permutation applied. This is equivalent to a column reordering ... See full document
8
Finding Parts in Very Large Corpora
... Pattern A headlight windshield ignition shifter dashboard radiator brake tailpipe pipe airbag speedometer converter hood trunk visor vent wheel occupant engine tyre Pattern B trunk wheel[r] ... See full document
8
An Efficient Syntactic Tagging Tool for Corpora
... AN EFFICIENT SYNTACTIC TAGGING TOOL FOR CORPORA A N E F F I C I E N T S Y N T A C T I C T A G G I N G T O O L F O R C O R P O R A @ Ming Zhou Changning Huang Dept o f Computer Science, "l~inghua Unive[.] ... See full document
7
SB@GU at the Complex Word Identification 2018 Shared Task
... from N-Watch include frequency in- formation from the British National Corpus (BNC), the English part of CELEX, the Kuˇcera and Francis list (KF), the Sydney Morning Herald (SMH); reaction times and bi- and ... See full document
7
Using sub word n gram models for dealing with OOV in large vocabulary speech recognition for Latvian
... Because of these properties, one word in Latvian can have tens or even hundreds (in the case of verbs) of surface forms. A successful large vocabulary speech recognition system must be able to recognize most (if ... See full document
5
HYBRID OPTIMIZATION FOR GRID SCHEDULING USING GENETIC ALGORITHM WITH LOCAL SEARCH
... Table 3 shows the list of ad-hoc query and the average precision value of expansion, enrich and combination of expansion and enrich. It is shown that the query at ID 3, 4, 5, 9 and 14 have improvement up to 13% ... See full document
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