Robust garden path parsing

2004 ◽  
Vol 10 (1) ◽  
pp. 1-24 ◽  
Author(s):  
BRIAN ROARK
Keyword(s):  
Ad Hoc ◽  
Search Space ◽  
Complete Coverage ◽  
Context Free Grammar ◽  
Garden Path ◽  
Empirical Results ◽  
Newspaper Text ◽  
Context Free ◽  
Probabilistic Context ◽  
Excellent Accuracy

This paper presents modifications to a standard probabilistic context-free grammar that enable a predictive parser to avoid garden pathing without resorting to any ad-hoc heuristic repair. The resulting parser is shown to apply efficiently to both newspaper text and telephone conversations with complete coverage and excellent accuracy. The distribution over trees is peaked enough to allow the parser to find parses efficiently, even with the much larger search space resulting from overgeneration. Empirical results are provided for both Wall St. Journal and Switchboard test corpora.

scholarly journals Unsupervised Grammar Induction with Depth-bounded PCFG

2018 ◽  
Vol 6 ◽  
pp. 211-224 ◽  
Author(s):  
Lifeng Jin ◽  
Finale Doshi-Velez ◽  
Timothy Miller ◽  
William Schuler ◽  
Lane Schwartz
Keyword(s):  
Parameter Space ◽  
Search Space ◽  
Grammar Induction ◽  
Context Free Grammar ◽  
Grammar Acquisition ◽  
Recent Interest ◽  
Category Labels ◽  
Context Free ◽  
Probabilistic Context

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.

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 Spurious Ambiguity and Focalization

2018 ◽  
Vol 44 (2) ◽  
pp. 285-327 ◽  
Author(s):  
Glyn Morrill ◽  
Oriol Valentín
Keyword(s):  
Mathematical Structure ◽  
Search Space ◽  
Categorial Grammar ◽  
Context Free Grammar ◽  
Ordered Trees ◽  
Proof Nets ◽  
Theoretic Technique ◽  
Context Free

Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize. In this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.

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
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