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Hits 241 – 257 of 257

241
Extending Topic Models With Syntax and Semantics Relationships
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242
Dialog between a Lexicographer and a Translator
In: Cognitive Studies | Études cognitives; No 13 (2013); 13-23 ; 2392-2397 (2015)
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243
A Python-based Interface for Wide Coverage Lexicalized Tree-adjoining Grammars
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 139-159 (2015) (2015)
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244
Domain Adaptation for Machine Translation with Instance Selection
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 5-20 (2015) (2015)
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245
Scalable Reordering Models for SMT based on Multiclass SVM
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 65-84 (2015) (2015)
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246
Evaluating Machine Translation Quality Using Short Segments Annotations
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 85-110 (2015) (2015)
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247
Evaluating MT systems with BEER
In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 17-26 (2015) (2015)
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248
Exact Expected Average Precision of the Random Baseline for System Evaluation
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 131-138 (2015) (2015)
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249
Resources for Indonesian Sentiment Analysis
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 21-41 (2015) (2015)
Abstract: In this work, we present subjectivity lexicons of positive and negative expressions for Indonesian language created by automatically translating English lexicons. Other variations are created by intersecting or unioning them. We compare the lexicons in the task of predicting sentence polarity on a set of 446 manually annotated sentences and we also contrast the generic lexicons with a small lexicon extracted directly from the annotated sentences (in a cross-validation setting). We seek for further improvements by assigning weights to lexicon entries and by wrapping the prediction into a machine learning task with a small number of additional features. We observe that lexicons are able to reach high recall but suffer from low precision when predicting whether a sentence is evaluative (positive or negative) or not (neutral). Weighting the lexicons can improve either the recall or the precision but with a comparable decrease in the other measure.
Keyword: Computational linguistics. Natural language processing; P98-98.5
URL: https://doi.org/10.1515/pralin-2015-0002
https://doaj.org/article/5e0256722ffb4458a5b9bcc5da402cdc
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250
Ultrametric Distance in Syntax
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 111-130 (2015) (2015)
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251
Joshua 6: A phrase-based and hierarchical statistical machine translation system
In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 5-16 (2015) (2015)
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252
Sampling Phrase Tables for the Moses Statistical Machine Translation System
In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 39-50 (2015) (2015)
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253
TmTriangulate: A Tool for Phrase Table Triangulation
In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 75-86 (2015) (2015)
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254
MT-ComparEval: Graphical evaluation interface for Machine Translation development
In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 63-74 (2015) (2015)
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255
Box: Natural Language Processing Research Using Amazon Web Services
In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 27-38 (2015) (2015)
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256
QuEst for High Quality Machine Translation
In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 43-64 (2015) (2015)
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257
Grasp: Randomised Semiring Parsing
In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 51-62 (2015) (2015)
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