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Journal articles on the topic 'Probabilistic grammar'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Nitay, Dolav, Dana Fisman, and Michal Ziv-Ukelson. "Learning of Structurally Unambiguous Probabilistic Grammars." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9170–78. http://dx.doi.org/10.1609/aaai.v35i10.17107.

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The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic grammars in particular, most of the literature has concentrated on the second problem. In this work we address the first problem. We restrict attention to structurally unambiguous weighted context-free grammars (SUWCFG) and provide a query learning algorithm for strucuturally unambiguous probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be represented using co-linear multiplicity tree automata (CMTA), and provide a polynomial learning algorithm that learns CMTAs. We show that the learned CMTA can be converted into a probabilistic grammar, thus providing a complete algorithm for learning a strucutrally unambiguous probabilistic context free grammar (both the grammar topology and the probabilistic weights) using structured membership queries and structured equivalence queries. We demonstrate the usefulness of our algorithm in learning PCFGs over genomic data.

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2

KROTOV, ALEXANDER, MARK HEPPLE, ROBERT GAIZAUSKAS, and YORICK WILKS. "Evaluating two methods for Treebank grammar compaction." Natural Language Engineering 5, no. 4 (December 1999): 377–94. http://dx.doi.org/10.1017/s1351324900002308.

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Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be ‘read off’ the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar. In this paper, we explore ways by which a treebank grammar can be reduced in size or ‘compacted’, which involve the use of two kinds of technique: (i) thresholding of rules by their number of occurrences; and (ii) a method of rule-parsing, which has both probabilistic and non-probabilistic variants. Our results show that by a combined use of these two techniques, a probabilistic context-free grammar can be reduced in size by 62% without any loss in parsing performance, and by 71% to give a gain in recall, but some loss in precision.

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Benedikt Szmrecsanyi. "Diachronic Probabilistic Grammar." English Language and Linguistics 19, no. 3 (December 2013): 41–68. http://dx.doi.org/10.17960/ell.2013.19.3.002.

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4

Daland, Robert. "Long words in maximum entropy phonotactic grammars." Phonology 32, no. 3 (December 2015): 353–83. http://dx.doi.org/10.1017/s0952675715000251.

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A phonotactic grammar assigns a well-formedness score to all possible surface forms. This paper considers whether phonotactic grammars should be probabilistic, and gives several arguments that they need to be. Hayes & Wilson (2008) demonstrate the promise of a maximum entropy Harmonic Grammar as a probabilistic phonotactic grammar. This paper points out a theoretical issue with maxent phonotactic grammars: they are not guaranteed to assign a well-defined probability distribution, because sequences that contain arbitrary repetitions of unmarked sequences may be underpenalised. The paper motivates a solution to this issue: include a *Structconstraint. A mathematical proof of necessary and sufficient conditions to avoid the underpenalisation problem are given in online supplementary materials.

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Shih, Stephanie S. "Constraint conjunction in weighted probabilistic grammar." Phonology 34, no. 2 (August 2017): 243–68. http://dx.doi.org/10.1017/s0952675717000136.

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This paper examines a key difference between constraint conjunction and constraint weight additivity, arguing that the two do not have the same empirical coverage. In particular, constraint conjunction in weighted probabilistic grammar allows for superadditive constraint interaction, where the effect of violating two constraints goes beyond the additive combination of the two constraints’ weights alone. A case study from parasitic tone harmony in Dioula d'Odienné demonstrates superadditive local and long-distance segmental feature similarities that increase the likelihood of tone harmony. Superadditivity in Dioula d'Odienné is formally captured in Maximum Entropy Harmonic Grammar by weighted constraint conjunction. Counter to previous approaches that supplant constraint conjunction with weight additivity in Harmonic Grammar, information-theoretic model comparison reveals that weighted constraint conjunction improves the grammar's explanatory power when modelling quantitative natural language patterns.

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CASACUBERTA, FRANCISCO. "GROWTH TRANSFORMATIONS FOR PROBABILISTIC FUNCTIONS OF STOCHASTIC GRAMMARS." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 03 (May 1996): 183–201. http://dx.doi.org/10.1142/s0218001496000153.

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Stochastic Grammars are the most usual models in Syntactic Pattern Recognition. Both components of a Stochastic Grammar, the characteristic grammar and the probabilities attached to the rules, can be learnt automatically from training samples. In this paper, first a review of some algorithms are presented to infer the probabilistic component of Stochastic Regular and Context-Free Grammars under the framework of the Growth Transformations. On the other hand, with Stochastic Grammars, the patterns must be represented as strings over a finite set of symbols. However, the most natural representation in many Syntactic Pattern Recognition applications (i.e. speech) is as sequences of vectors from a feature vector space, that is, a continuous representation. Therefore, to obtain a discrete representation of the patterns, some quantization errors are introduced in the representation process. To avoid this drawback, a formal presentation of a semi-continuous extension of the Stochastic Regular and Context-Free Grammars is studied and probabilistic estimation algorithms are developed in this paper. In this extension, sequences of vectors, instead of strings of symbols, can be processed with Stochastic Grammars.

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Han, Young S., and Key-Sun Choi. "Best parse parsing with Earley's and Inside algorithms on probabilistic RTN." Natural Language Engineering 1, no. 2 (June 1995): 147–61. http://dx.doi.org/10.1017/s1351324900000127.

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AbstractInside parsing is a best parse parsing method based on the Inside algorithm that is often used in estimating probabilistic parameters of stochastic context free grammars. It gives a best parse in O(N3G3) time where N is the input size and G is the grammar size. Earley algorithm can be made to return best parses with the same complexity in N.By way of experiments, we show that Inside parsing can be more efficient than Earley parsing with sufficiently large grammar and sufficiently short input sentences. For instance, Inside parsing is better with sentences of 16 or less words for a grammar containing 429 states. In practice, parsing can be made efficient by employing the two methods selectively.The redundancy of Inside algorithm can be reduced by the topdown filtering using the chart produced by Earley algorithm, which is useful in training the probabilistic parameters of a grammar. Extensive experiments on Penn Tree corpus show that the efficiency of Inside computation can be improved by up to 55%.

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8

Kita, Kenji. "Mixture Probabilistic Context-Free Grammar." Journal of Natural Language Processing 3, no. 4 (1996): 103–13. http://dx.doi.org/10.5715/jnlp.3.4_103.

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9

DAI, Yin-Tang, Cheng-Rong WU, Sheng-Xiang MA, and Yi-Ping ZHONG. "Hierarchically Classified Probabilistic Grammar Parsing." Journal of Software 22, no. 2 (March 25, 2011): 245–57. http://dx.doi.org/10.3724/sp.j.1001.2011.03809.

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10

Arthi, K., and Kamala Krithivasan. "Probabilistic Parallel Communicating Grammar Systems." International Journal of Computer Mathematics 79, no. 1 (January 2002): 1–26. http://dx.doi.org/10.1080/00207160211914.

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11

Lioutikov, Rudolf, Guilherme Maeda, Filipe Veiga, Kristian Kersting, and Jan Peters. "Learning attribute grammars for movement primitive sequencing." International Journal of Robotics Research 39, no. 1 (November 17, 2019): 21–38. http://dx.doi.org/10.1177/0278364919868279.

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Movement primitives are a well studied and widely applied concept in modern robotics. However, composing primitives out of an existing library has shown to be a challenging problem. We propose the use of probabilistic context-free grammars to sequence a series of primitives to generate complex robot policies from a given library of primitives. The rule-based nature of formal grammars allows an intuitive encoding of hierarchically structured tasks. This hierarchical concept strongly connects with the way robot policies can be learned, organized, and re-used. However, the induction of context-free grammars has proven to be a complicated and yet unsolved challenge. We exploit the physical nature of robot movement primitives to restrict and efficiently search the grammar space. The grammar is learned by applying a Markov chain Monte Carlo optimization over the posteriors of the grammars given the observations. The proposal distribution is defined as a mixture over the probabilities of the operators connecting the search space. Moreover, we present an approach for the categorization of probabilistic movement primitives and discuss how the connectibility of two primitives can be determined. These characteristics in combination with restrictions to the operators guarantee continuous sequences while reducing the grammar space. In addition, a set of attributes and conditions is introduced that augments probabilistic context-free grammars in order to solve primitive sequencing tasks with the capability to adapt single primitives within the sequence. The method was validated on tasks that require the generation of complex sequences consisting of simple movement primitives using a seven-degree-of-freedom lightweight robotic arm.

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BENDER, EMILY M. "Socially meaningful syntactic variation in sign-based grammar." English Language and Linguistics 11, no. 2 (July 2007): 347–81. http://dx.doi.org/10.1017/s1360674307002286.

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In this article, I investigate the implications of socially meaningful sociolinguistic variation for competence grammar, working from the point of view of HPSG as a kind of performance-plausible sign-based grammar. Taking data from African American Vernacular English variable copula absence as a case study, I argue that syntactic constraints and social meaning are intertwined. I present an overview of the literature on social meaning, discuss what grammars are models of, and argue that in order to model socially meaningful variation, competence grammars need to be extended to include social meaning, precompiled phrases, and probabilistic or frequentistic information. I then explore different heuristics for defining the boundaries of competence grammar and discuss the commonalities between the proposed additions and the kind of linguistic knowledge which is generally assumed to comprise competence grammar.

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Winters, Thomas, Giuseppe Marra, Robin Manhaeve, and Luc De Raedt. "DeepStochLog: Neural Stochastic Logic Programming." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10090–100. http://dx.doi.org/10.1609/aaai.v36i9.21248.

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Recent advances in neural-symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural-symbolic framework based on stochastic definite clause grammars, a kind of stochastic logic program. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs. Furthermore, the experimental evaluation shows that DeepStochLog achieves state-of-the-art results on challenging neural-symbolic learning tasks.

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Mukhtar, Neelam, Mohammad Abid Khan, and Fatima TuzZuhra. "Probabilistic Context Free Grammar for Urdu." Linguistics and Literature Review 2, no. 2 (October 2016): 109–16. http://dx.doi.org/10.32350/llr.22.04.

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15

Whiting, Mark E., Jonathan Cagan, and Philip LeDuc. "Efficient probabilistic grammar induction for design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, no. 2 (May 2018): 177–88. http://dx.doi.org/10.1017/s0890060417000464.

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AbstractThe use of grammars in design and analysis has been set back by the lack of automated ways to induce them from arbitrarily structured datasets. Machine translation methods provide a construct for inducing grammars from coded data which have been extended to be used for design through pre-coded design data. This work introduces a four-step process for inducing grammars from un-coded structured datasets which can constitute a wide variety of data types, including many used in the design. The method includes: (1) extracting objects from the data, (2) forming structures from objects, (3) expanding structures into rules based on frequency, and (4) finding rule similarities that lead to consolidation or abstraction. To evaluate this method, grammars are induced from generated data, architectural layouts and three-dimensional design models to demonstrate that this method offers usable grammars automatically which are functionally similar to grammars produced by hand.

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Nederhof, Mark-Jan. "A General Technique to Train Language Models on Language Models." Computational Linguistics 31, no. 2 (June 2005): 173–85. http://dx.doi.org/10.1162/0891201054223986.

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We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a probabilistic context-free grammar, such that the Kullback-Leibler distance between grammar and trained automaton is provably minimal. This is a substantial generalization of an existing algorithm to train an n-gram model on the basis of a probabilistic context-free grammar.

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17

SRINIVAS, B. "Explanation-based learning and finite state transducers: applications to parsing lexicalized tree adjoining grammars." Natural Language Engineering 2, no. 4 (December 1996): 367–68. http://dx.doi.org/10.1017/s1351324997001642.

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There are currently two philosophies for building grammars and parsers: hand-crafted, wide coverage grammars; and statistically induced grammars and parsers. Aside from the methodological differences in grammar construction, the linguistic knowledge which is overt in the rules of handcrafted grammars is hidden in the statistics derived by probabilistic methods, which means that generalizations are also hidden and the full training process must be repeated for each domain. Although handcrafted wide coverage grammars are portable, they can be made more efficient when applied to limited domains, if it is recognized that language in limited domains is usually well constrained and certain linguistic constructions are more frequent than others. We view a domain-independent grammar as a repository of portable grammatical structures whose combinations are to be specialized for a given domain. We use Explanation-Based Learning (EBL) to identify the relevant subset of a handcrafted general purpose grammar (XTAG) needed to parse in a given domain (ATIS). We exploit the key properties of Lexicalized Tree-Adjoining Grammars to view parsing in a limited domain as finite state transduction from strings to their dependency structures.

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18

Jin, Lifeng, Finale Doshi-Velez, Timothy Miller, William Schuler, and Lane Schwartz. "Unsupervised Grammar Induction with Depth-bounded PCFG." Transactions of the Association for Computational Linguistics 6 (December 2018): 211–24. http://dx.doi.org/10.1162/tacl_a_00016.

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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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Claes, Jeroen. "Probabilistic Grammar: The view from Cognitive Sociolinguistics." Glossa: a journal of general linguistics 2, no. 1 (June 29, 2017): 62. http://dx.doi.org/10.5334/gjgl.298.

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20

Lindemann, Eric. "Virtual instrument player using probabilistic gesture grammar." Journal of the Acoustical Society of America 130, no. 4 (October 2011): 2432. http://dx.doi.org/10.1121/1.3654750.

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21

BRESNAN, JOAN, ASHWINI DEO, and DEVYANI SHARMA. "Typology in variation: a probabilistic approach to be and n't in the Survey of English Dialects." English Language and Linguistics 11, no. 2 (July 2007): 301–46. http://dx.doi.org/10.1017/s1360674307002274.

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Variation within grammars is a reflection of variation between grammars. Subject agreement and synthetic negation for the verb be show extraordinary local variation in the Survey of English Dialects (Orton et al., 1962–71). Extracting partial grammars of individuals, we confirm leveling patterns across person, number, and negation (Ihalainen, 1991; Cheshire, Edwards & Whittle, 1993; Cheshire, 1996). We find that individual variation bears striking structural resemblances to invariant dialect paradigms, and also reflects typologically observed markedness properties (Aissen, 1999). In the framework of Stochastic Optimality Theory (Boersma & Hayes, 2001), variable outputs of individual speakers are expected to be constrained by the same typological and markedness generalizations found crosslinguistically. The stochastic evaluation of candidate outputs in individual grammars reranks individual constraints by perturbing their ranking values, with the potential for stable variation between two near-identical rankings. The stochastic learning mechanism is sensitive to variable frequencies encountered in the linguistic environment, whether in geographical or social space. In addition to relating individual and group dialectal variation to typological variation (Kortmann, 1999; Anderwald, 2003), the findings suggest that an individual grammar is sensitively tuned to frequencies in the linguistic environment, leading to isolated loci of variability in the grammar rather than complete alternations of paradigms. A characteristic of linguistic variation that has emerged in distinct fields of enquiry is that variation within a single grammar bears a close resemblance to variation across grammars. Sociolinguistic studies, for instance, have long observed that ‘variation within the speech of a single speaker derives from the variation which exists between speakers’ (Bell, 1984: 151). In the present study, individual patterns of variation in subject–verb agreement with affirmative and negative be extracted from the Survey of English Dialects(SED, Orton et al., 1962–71) show striking structural resemblances to patterns of interdialectal, or categorical, variation.

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22

Keller, Frank, and Ash Asudeh. "Probabilistic Learning Algorithms and Optimality Theory." Linguistic Inquiry 33, no. 2 (April 2002): 225–44. http://dx.doi.org/10.1162/002438902317406704.

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This article provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilistic optimality-theoretic (OT) grammars proposed by Boersma and Hayes (2001). We discuss the limitations of a standard algorithm for OT learning and outline how the GLA attempts to overcome these limitations. We point out a number of serious shortcomings with the GLA: (a) A methodological problem is that the GLA has not been tested on unseen data, which is standard practice in computational language learning. (b) We provide counterexamples, that is, attested data sets that the GLA is not able to learn. (c) Essential algorithmic properties of the GLA (correctness and convergence) have not been proven formally. (d) By modeling frequency distributions in the grammar, the GLA conflates the notions of competence and performance. This leads to serious conceptual problems, as OT crucially relies on the competence/performance distinction.

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Kato, Yoshihide, Shigeki Matsubara, Katsuhiko Toyama, and Yasuyoshi Inagaki. "Incremental Parsing Based on Probabilistic Context Free Grammar." IEEJ Transactions on Electronics, Information and Systems 122, no. 12 (2002): 2109–19. http://dx.doi.org/10.1541/ieejeiss1987.122.12_2109.

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24

Khoufi, Nabil, Chafik Aloulou, and Lamia Hadrich Belguith. "Parsing Arabic using induced probabilistic context free grammar." International Journal of Speech Technology 19, no. 2 (September 4, 2015): 313–23. http://dx.doi.org/10.1007/s10772-015-9300-x.

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25

Liu, Tianqiang, Siddhartha Chaudhuri, Vladimir G. Kim, Qixing Huang, Niloy J. Mitra, and Thomas Funkhouser. "Creating consistent scene graphs using a probabilistic grammar." ACM Transactions on Graphics 33, no. 6 (November 19, 2014): 1–12. http://dx.doi.org/10.1145/2661229.2661243.

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26

Li, Dan, Disheng Hu, Yuke Sun, and Yingsong Hu. "3D scene reconstruction using a texture probabilistic grammar." Multimedia Tools and Applications 77, no. 21 (May 1, 2018): 28417–40. http://dx.doi.org/10.1007/s11042-018-6052-z.

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27

Clark, Alexander, and Nathanaël Fijalkow. "Consistent Unsupervised Estimators for Anchored PCFGs." Transactions of the Association for Computational Linguistics 8 (July 2020): 409–22. http://dx.doi.org/10.1162/tacl_a_00323.

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Learning probabilistic context-free grammars (PCFGs) from strings is a classic problem in computational linguistics since Horning ( 1969 ). Here we present an algorithm based on distributional learning that is a consistent estimator for a large class of PCFGs that satisfy certain natural conditions including being anchored (Stratos et al., 2016 ). We proceed via a reparameterization of (top–down) PCFGs that we call a bottom–up weighted context-free grammar. We show that if the grammar is anchored and satisfies additional restrictions on its ambiguity, then the parameters can be directly related to distributional properties of the anchoring strings; we show the asymptotic correctness of a naive estimator and present some simulations using synthetic data that show that algorithms based on this approach have good finite sample behavior.

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Carter, Ronald, and Michael McCarthy. "The English get-passive in spoken discourse: description and implications for an interpersonal grammar." English Language and Linguistics 3, no. 1 (May 1999): 41–58. http://dx.doi.org/10.1017/s136067439900012x.

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Using a 1.5-million-word sample from the CANCODE spoken English corpus, we present a description of the get-passive in informal spoken British English. Previous studies of the get-passive are reviewed, and their focus on contextual and interpersonal meanings is noted. A number of related structures are then considered and the possibility of a passive gradient is discussed. The corpus sample contains 139 get-passives of the type X get + past participle (by Y) (e.g. He got killed), of which 124 occur in contexts interpreted as adversative or problematic from the speaker's viewpoint. Very few examples contain an explicit agent or adverbials. Main verb frequency is also considered. Where contexts are positive rather than adversative, newsworthiness or focus of some kind on the subject and/or events is still apparent. The corpus evidence is used to evaluate the terms upon which an interpersonal grammar of English might be developed, and a contrast is drawn between deterministic grammars and probabilistic ones, with probabilistic grammars offering the best potential for the understanding of interpersonal features.

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Auger, Julie. "Phonological variation and Optimality Theory: Evidence from word-initial vowel epenthesis in Vimeu Picard." Language Variation and Change 13, no. 3 (October 2001): 253–303. http://dx.doi.org/10.1017/s0954394501133016.

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One striking feature of Vimeu Picard concerns the regular insertion of epenthetic vowels in order to break up consonant clusters and to syllabify word-initial and word-final consonants. This corpus-based study focuses on word-initial epenthesis. It provides quantitative evidence that vowel epenthesis applies categorically in some environments and variably in others. Probabilistic analysis demonstrates that the variable pattern is constrained by a complex interplay of linguistic factors. Following Labov (1972a, 1972b) and Antilla and Cho (1998), I interpret such intricate grammatical conditioning as evidence that this variation is a reflection of a grammatical competence that generates both categorical and variable outputs, and I propose an account within the framework of Optimality Theory. An analysis of individual patterns of epenthesis by members of the community reveals that, even though all speakers share the same basic community grammar, their use of epenthesis differs qualitatively as well as quantitatively. I show that individual grammars can be derived from the community grammar, and that Optimality Theory allows us to formalize the idea that individual grammars constitute more specific versions of community grammars.

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Khoufi, Nabil, Chafik Aloulou, and Lamia Hadrich Belguith. "Arabic Probabilistic Context Free Grammar Induction from a Treebank." Research in Computing Science 90, no. 1 (December 31, 2015): 77–86. http://dx.doi.org/10.13053/rcs-90-1-6.

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Ren, Gang, Zhe Wen, Xuchen Yang, Cheng Shu, Fangyu Ke, and Mark Bocko. "Representation of musical performance “grammar” using probabilistic graphical models." Journal of the Acoustical Society of America 134, no. 5 (November 2013): 3995. http://dx.doi.org/10.1121/1.4830573.

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32

vor der Brück, Tim. "A Probabilistic Approach to Error Detection&Correction for Tree-Mapping Grammars." Prague Bulletin of Mathematical Linguistics 111, no. 1 (October 1, 2018): 97–112. http://dx.doi.org/10.2478/pralin-2018-0009.

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Abstract Rule-based natural language generation denotes the process of converting a semantic input structure into a surface representation by means of a grammar. In the following, we assume that this grammar is handcrafted and not automatically created for instance by a deep neural network. Such a grammar might comprise of a large set of rules. A single error in these rules can already have a large impact on the quality of the generated sentences, potentially causing even a complete failure of the entire generation process. Searching for errors in these rules can be quite tedious and time-consuming due to potentially complex and recursive dependencies. This work proposes a statistical approach to recognizing errors and providing suggestions for correcting certain kinds of errors by cross-checking the grammar with the semantic input structure. The basic assumption is the correctness of the latter, which is usually a valid hypothesis due to the fact that these input structures are often automatically created. Our evaluation reveals that in many cases an automatic error detection and correction is indeed possible.

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Long Zhu, Yuanhao Chen, and A. Yuille. "Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 1 (January 2009): 114–28. http://dx.doi.org/10.1109/tpami.2008.67.

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Szmrecsanyi, Benedikt, Jason Grafmiller, Benedikt Heller, and Melanie Röthlisberger. "Around the world in three alternations." English World-Wide 37, no. 2 (June 24, 2016): 109–37. http://dx.doi.org/10.1075/eww.37.2.01szm.

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We sketch a project that marries probabilistic grammar research to scholarship on World Englishes, thus synthesizing two previously rather disjoint lines of research into one unifying project with a coherent focus. This synthesis is hoped to advance usage-based theoretical linguistics by adopting a large-scale comparative and sociolinguistically responsible perspective on grammatical variation. To highlight the descriptive and theoretical benefits of the approach, we present case studies of three syntactic alternations (the particle placement, genitive, and dative alternations) in four varieties of English (British, Canadian, Indian, and Singapore), as represented in the International Corpus of English. We report that the varieties studied share a core probabilistic grammar which is, however, subject to indigenization at various degrees of subtlety, depending on the abstractness of the syntactic patterns studied.

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Dimopoulos, Alexandros C., Christos Pavlatos, and George Papakonstantinou. "Hardware Inexact Grammar Parser." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 11 (April 11, 2017): 1759025. http://dx.doi.org/10.1142/s021800141759025x.

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In this paper, a platform is presented, that given a Stochastic Context-Free Grammar (SCFG), automatically outputs the description of a parser in synthesizable Hardware Description Language (HDL) which can be downloaded in an FPGA (Field Programmable Gate Arrays) board. Although the proposed methodology can be used for various inexact models, the probabilistic model is analyzed in detail and the extension to other inexact schemes is described. Context-Free Grammars (CFG) are augmented with attributes which represent the probability values. Initially, a methodology is proposed based on the fact that the probabilities can be evaluated concurrently with the parsing during the parse table construction by extending the fundamental parsing operation proposed by Chiang & Fu. Using this extended operation, an efficient architecture is presented based on Earley’s parallel algorithm, which given an input string, generates the parse table while evaluating concurrently the probabilities of the generated dotted grammar rules in the table. Based on this architecture, a platform has been implemented that automatically generates the hardware design of the parser given a SCFG. The platform is suitable for embedded systems applications where a natural language interface is required or in pattern recognition tasks. The proposed hardware platform has been tested for various SCFGs and was compared with previously presented hardware parser for SCFGs based on Earley’s parallel algorithm. The hardware generated by the proposed platform is much less complicated than the one of comparison and succeeds a speed-up of one order of magnitude.

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Goldsmith, John. "Unsupervised Learning of the Morphology of a Natural Language." Computational Linguistics 27, no. 2 (June 2001): 153–98. http://dx.doi.org/10.1162/089120101750300490.

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This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist. In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.

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Nguyen, Dang Tuan, Kiet Van Nguyen, and Tin Trung Pham. "Implementing A Subcategorized Probabilistic Definite Clause Grammar for Vietnamese Sentence Parsing." International Journal on Natural Language Computing 2, no. 4 (August 31, 2013): 1–19. http://dx.doi.org/10.5121/ijnlc.2013.2401.

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Hasegawa, Yoshihiko, and Hitoshi Iba. "Estimation of Distribution Algorithm Based on Probabilistic Grammar with Latent Annotations." Transactions of the Japanese Society for Artificial Intelligence 23 (2008): 13–26. http://dx.doi.org/10.1527/tjsai.23.13.

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Mazur, Zygmunt, and Janusz Pec. "The Use of Context-Free Probabilistic Grammar to Anonymise Statistical Data." Cybernetics and Systems 51, no. 2 (January 10, 2020): 177–91. http://dx.doi.org/10.1080/01969722.2019.1705551.

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Wong, Pak-Kan, Kwong-Sak Leung, and Man-Leung Wong. "Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia." Expert Systems with Applications 135 (November 2019): 237–48. http://dx.doi.org/10.1016/j.eswa.2019.06.012.

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Chauve, Cedric, Julien Courtiel, and Yann Ponty. "Counting, Generating, Analyzing and Sampling Tree Alignments." International Journal of Foundations of Computer Science 29, no. 05 (August 2018): 741–67. http://dx.doi.org/10.1142/s0129054118420030.

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Pairwise ordered tree alignment are combinatorial objects that appear in important applications, such as RNA secondary structure comparison. However, the usual representation of tree alignments as supertrees is ambiguous, i.e. two distinct supertrees may induce identical sets of matches between identical pairs of trees. This ambiguity is uninformative, and detrimental to any probabilistic analysis. In this work, we consider tree alignments up to equivalence. Our first result is a precise asymptotic enumeration of tree alignments, obtained from a context-free grammar by mean of basic analytic combinatorics. Our second result focuses on alignments between two given ordered trees [Formula: see text] and [Formula: see text]. By refining our grammar to align specific trees, we obtain a decomposition scheme for the space of alignments, and use it to design an efficient dynamic programming algorithm for sampling alignments under the Gibbs-Boltzmann probability distribution. This generalizes existing tree alignment algorithms, and opens the door for a probabilistic analysis of the space of suboptimal alignments.

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Veras, Rafael, Christopher Collins, and Julie Thorpe. "A Large-Scale Analysis of the Semantic Password Model and Linguistic Patterns in Passwords." ACM Transactions on Privacy and Security 24, no. 3 (August 31, 2021): 1–21. http://dx.doi.org/10.1145/3448608.

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In this article, we present a thorough evaluation of semantic password grammars. We report multifactorial experiments that test the impact of sample size, probability smoothing, and linguistic information on password cracking. The semantic grammars are compared with state-of-the-art probabilistic context-free grammar ( PCFG ) and neural network models, and tested in cross-validation and A vs. B scenarios. We present results that reveal the contributions of part-of-speech (syntactic) and semantic patterns, and suggest that the former are more consequential to the security of passwords. Our results show that in many cases PCFGs are still competitive models compared to their latest neural network counterparts. In addition, we show that there is little performance gain in training PCFGs with more than 1 million passwords. We present qualitative analyses of four password leaks (Mate1, 000webhost, Comcast, and RockYou) based on trained semantic grammars, and derive graphical models that capture high-level dependencies between token classes. Finally, we confirm the similarity inferences from our qualitative analysis by examining the effectiveness of grammars trained and tested on all pairs of leaks.

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Fraser, Alexander, Helmut Schmid, Richárd Farkas, Renjing Wang, and Hinrich Schütze. "Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language." Computational Linguistics 39, no. 1 (March 2013): 57–85. http://dx.doi.org/10.1162/coli_a_00135.

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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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ten Cate, Carel, and Kazuo Okanoya. "Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1598 (July 19, 2012): 1984–94. http://dx.doi.org/10.1098/rstb.2012.0055.

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The domain of syntax is seen as the core of the language faculty and as the most critical difference between animal vocalizations and language. We review evidence from spontaneously produced vocalizations as well as from perceptual experiments using artificial grammars to analyse animal syntactic abilities, i.e. abilities to produce and perceive patterns following abstract rules. Animal vocalizations consist of vocal units (elements) that are combined in a species-specific way to create higher order strings that in turn can be produced in different patterns. While these patterns differ between species, they have in common that they are no more complex than a probabilistic finite-state grammar. Experiments on the perception of artificial grammars confirm that animals can generalize and categorize vocal strings based on phonetic features. They also demonstrate that animals can learn about the co-occurrence of elements or learn simple ‘rules’ like attending to reduplications of units. However, these experiments do not provide strong evidence for an ability to detect abstract rules or rules beyond finite-state grammars. Nevertheless, considering the rather limited number of experiments and the difficulty to design experiments that unequivocally demonstrate more complex rule learning, the question of what animals are able to do remains open.

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SHIRAI, KIYOAKI, TAKENOBU TOKUNAGA, and HOZUMI TANAKA. "Automatic Extraction of Japanese Probabilistic Context Free Grammar From a Bracketed Corpus." Journal of Natural Language Processing 4, no. 1 (1997): 125–46. http://dx.doi.org/10.5715/jnlp.4.125.

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Bod, Rens. "From Exemplar to Grammar: A Probabilistic Analogy-Based Model of Language Learning." Cognitive Science 33, no. 5 (April 8, 2009): 752–93. http://dx.doi.org/10.1111/j.1551-6709.2009.01031.x.

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Demberg, Vera, Frank Keller, and Alexander Koller. "Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar." Computational Linguistics 39, no. 4 (December 2013): 1025–66. http://dx.doi.org/10.1162/coli_a_00160.

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Psycholinguistic research shows that key properties of the human sentence processor are incrementality, connectedness (partial structures contain no unattached nodes), and prediction (upcoming syntactic structure is anticipated). There is currently no broad-coverage parsing model with these properties, however. In this article, we present the first broad-coverage probabilistic parser for PLTAG, a variant of TAG that supports all three requirements. We train our parser on a TAG-transformed version of the Penn Treebank and show that it achieves performance comparable to existing TAG parsers that are incremental but not predictive. We also use our PLTAG model to predict human reading times, demonstrating a better fit on the Dundee eye-tracking corpus than a standard surprisal model.

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Jarosz, Gaja. "Learning with hidden structure in Optimality Theory and Harmonic Grammar: beyond Robust Interpretive Parsing." Phonology 30, no. 1 (May 2013): 27–71. http://dx.doi.org/10.1017/s0952675713000031.

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This paper explores the relative merits of constraint rankingvs. weighting in the context of a major outstanding learnability problem in phonology: learning in the face of hidden structure. Specifically, the paper examines a well-known approach to the structural ambiguity problem, Robust Interpretive Parsing (RIP; Tesar & Smolensky 1998), focusing on its stochastic extension first described by Boersma (2003). Two related problems with the stochastic formulation of RIP are revealed, rooted in a failure to take full advantage of probabilistic information available in the learner's grammar. To address these problems, two novel parsing strategies are introduced and applied to learning algorithms for both probabilistic ranking and weighting. The novel parsing strategies yield significant improvements in performance, asymmetrically improving performance of OT learners. Once RIP is replaced with the proposed modifications, the apparent advantage of HG over OT learners reported in previous work disappears (Boersma & Pater 2008).

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NAIGLES, LETITIA R. "Comprehension matters: a commentary on ‘A multiple process solution to the logical problem of language acquisition’." Journal of Child Language 31, no. 4 (November 2004): 936–40. http://dx.doi.org/10.1017/s0305000904006403.

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MacWhinney (2004) has provided a clear and welcome synthesis of many strands of the recent research addressing the logical problem of first language acquisition from a non-nativist or non-generative grammar framework. The strand that I will comment on is the one MacWhinney calls the ‘pivot’ of his proposal, namely, that acquiring a grammar is primarily a function of learning ITEM-BASEDPATTERNS (e.g. pp. 23–29, 41, passim). These item-based patterns serve a number of dominant roles within MacWhinney's proposal, including enforcing children's conservatism (thereby reducing greatly their overgeneralizations and need to recover from the same), supporting the probabilistic nature of grammar, and enabling the competition that promotes recovery from the overgeneralizations that do occur. My concern here is primarily with the first role, that of enforcing children's conservatism, and especially with the exclusive use of language PRODUCTION as the demonstrated support of this conservatism.

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Garcia, Guilherme D. "Weight gradience and stress in Portuguese." Phonology 34, no. 1 (May 2017): 41–79. http://dx.doi.org/10.1017/s0952675717000033.

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This paper examines the role of weight in stress assignment in the Portuguese lexicon, and proposes a probabilistic approach to stress. I show that weight effects are gradient, and weaken monotonically as we move away from the right edge of the word. Such effects depend on the position of a syllable in the word, as well as on the number of segments the syllable contains. The probabilistic model proposed in this paper is based on a single predictor, namely weight, and yields more accurate results than a categorical analysis, where weight is treated as binary. Finally, I discuss implications for the grammar of Portuguese.

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