241 |
Extending Topic Models With Syntax and Semantics Relationships
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242 |
Dialog between a Lexicographer and a Translator
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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
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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
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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
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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
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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
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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
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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
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In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 21-41 (2015) (2015)
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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.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doi.org/10.1515/pralin-2015-0002 https://doaj.org/article/5e0256722ffb4458a5b9bcc5da402cdc
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250 |
Ultrametric Distance in Syntax
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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
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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
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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
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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
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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
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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
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In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 43-64 (2015) (2015)
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257 |
Grasp: Randomised Semiring Parsing
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In: Prague Bulletin of Mathematical Linguistics , Vol 104, Iss 1, Pp 51-62 (2015) (2015)
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