Arabic Probabilistic Context Free Grammar Induction from a Treebank

There has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.

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

BMC Bioinformatics ◽

10.1186/s12859-021-04139-y ◽

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.

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

Computational Linguistics ◽

10.1162/coli_a_00135 ◽

2013 ◽

Vol 39 (1) ◽

pp. 57-85 ◽

Cited By ~ 2

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.

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