Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar

2019 ◽  
Author(s):  
Wenchao Du ◽  
Alan W Black
Keyword(s):  
Neural Response ◽  
Top Down ◽  
Context Free Grammar ◽  
Context Free ◽  
Probabilistic Context

scholarly journals Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Witold Dyrka ◽  
Marlena Gąsior-Głogowska ◽  
Monika Szefczyk ◽  
Natalia Szulc
Keyword(s):  
Functional Relationship ◽  
High Sensitivity ◽  
Alternative Methods ◽  
Discriminative Power ◽  
Context Free Grammar ◽  
Protein Motifs ◽  
Functional Features ◽  
Universal Grammars ◽  
Context Free ◽  
Probabilistic Context

Abstract Background Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs. Results First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship. Conclusions While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

scholarly journals Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language

2013 ◽  
Vol 39 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Alexander Fraser ◽  
Helmut Schmid ◽  
Richárd Farkas ◽  
Renjing Wang ◽  
Hinrich Schütze
Keyword(s):  
State Of The Art ◽  
Lessons Learned ◽  
Knowledge Sources ◽  
Lexical Knowledge ◽  
Context Free Grammar ◽  
The Impact ◽  
Context Free ◽  
Probabilistic Context

We study constituent parsing of German, a morphologically rich and less-configurational language. We use a probabilistic context-free grammar treebank grammar that has been adapted to the morphologically rich properties of German by markovization and special features added to its productions. We evaluate the impact of adding lexical knowledge. Then we examine both monolingual and bilingual approaches to parse reranking. Our reranking parser is the new state of the art in constituency parsing of the TIGER Treebank. We perform an analysis, concluding with lessons learned, which apply to parsing other morphologically rich and less-configurational languages.

scholarly journals A probabilistic context-free grammar for the detection of binding sites from a protein sequence

2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Witold Dyrka ◽  
Jean-Christophe Nebel
Keyword(s):  
Binding Sites ◽  
Protein Sequence ◽  
Context Free Grammar ◽  
Context Free ◽  
Probabilistic Context

scholarly journals Applying semantic-based probabilistic context-free grammar to medical language processing – A preliminary study on parsing medication sentences

2011 ◽  
Vol 44 (6) ◽  
pp. 1068-1075 ◽  
Author(s):  
Hua Xu ◽  
Samir AbdelRahman ◽  
Yanxin Lu ◽  
Joshua C. Denny ◽  
Son Doan
Keyword(s):  
Language Processing ◽  
Context Free Grammar ◽  
Medical Language ◽  
Medical Language Processing ◽  
Preliminary Study ◽  
Context Free ◽  
Probabilistic Context

A Natural Language Database Interface Based on a Probabilistic Context Free Grammar

2008 ◽  
Author(s):  
Bei-Bei Huang ◽  
Guigang Zhang ◽  
Phillip C-Y Sheu
Keyword(s):  
Natural Language ◽  
Context Free Grammar ◽  
Database Interface ◽  
Context Free ◽  
Probabilistic Context

scholarly journals Grammar Compression with Probabilistic Context-Free Grammar

2020 ◽  
Author(s):  
Hiroaki Naganuma ◽  
Diptarama Hendrian ◽  
Ryo Yoshinaka ◽  
Ayumi Shinohara ◽  
Naoki Kobayashi
Keyword(s):  
Context Free Grammar ◽  
Context Free ◽  
Probabilistic Context

scholarly journals Author Attribution of Arabic Texts Using Extended Probabilistic Context Free Grammar Language Model

2016 ◽  
Vol 8 (6) ◽  
pp. 27-39 ◽  
Author(s):  
Ibrahim S. I. Abuhaiba ◽  
◽  
Mohammad F. Eltibi
Keyword(s):  
Language Model ◽  
Context Free Grammar ◽  
Context Free ◽  
Probabilistic Context
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