The estimation of stochastic context-free grammars using the Inside-Outside algorithm

Lari, Kimia Bazargan; Young, S.

doi:10.1016/0885-2308(90)90022-x

Cited by 388 publications

(373 citation statements)

References 13 publications

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“…Recently, we have proposed a 1-D grammar learner [3], and have shown that the 1-D grammar learner acquires knowledge more effectively and runs faster than the inside-outside algorithm [13] 3 . Hence, we further extend the one-dimensional grammar learner to acquire a 2-D pCFG from twodimensional training records.…”

Section: Learning Two-dimensional Display Layout Using Probabilistic mentioning

confidence: 99%

Learning to Perceive Two-Dimensional Displays Using Probabilistic Grammars

Cohen

Koedinger

2012

Machine Learning and Knowledge Discovery in Databases

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Abstract. People learn to read and understand various displays (e.g., tables on webpages and software user interfaces) every day. How do humans learn to process such displays? Can computers be efficiently taught to understand and use such displays? In this paper, we use statistical learning to model how humans learn to perceive visual displays. We extend an existing probabilistic context-free grammar learner to support learning within a two-dimensional space by incorporating spatial and temporal information. Experimental results in both synthetic domains and real world domains show that the proposed learning algorithm is effective in acquiring user interface layout. Furthermore, we evaluate the effectiveness of the proposed algorithm within an intelligent tutoring agent, SimStudent, by integrating the learned display representation into the agent. Experimental results in learning complex problem solving skills in three domains show that the learned display representation is as good as one created by a human expert, in that skill learning using the learned representation is as effective as using a manually created representation.

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Section: Learning Two-dimensional Display Layout Using Probabilistic mentioning

confidence: 99%

Learning to Perceive Two-Dimensional Displays Using Probabilistic Grammars

Cohen

Koedinger

2012

Machine Learning and Knowledge Discovery in Databases

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“…The derivation is conceptually based on relevant frequency counting for discrete data which is a common practice for estimating PCFGs (Lari and Young 1990). In this section, we derive parameter estimation algorithm for the composite simplified lexical and semantic enhanced structural language model from the general EM algorithm (Dempster et al 1977).…”

Section: Training Algorithm For Simplified Lexical and Semantic Enhanmentioning

confidence: 99%

Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model

Wang

Cheng

et al. 2006

Grammatical Inference: Algorithms and Applications

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Abstract. In this paper, we present a directed Markov random field model that integrates trigram models, structural language models (SLM) and probabilistic latent semantic analysis (PLSA) for the purpose of statistical language modeling. The SLM is essentially a generalization of shift-reduce probabilistic push-down automata thus more complex and powerful than probabilistic context free grammars (PCFGs). The added context-sensitiveness due to trigrams and PLSAs and violation of tree structure in the topology of the underlying random field model make the inference and parameter estimation problems plausibly intractable, however the analysis of the behavior of the lexical and semantic enhanced structural language model leads to a generalized inside-outside algorithm and thus to rigorous exact EM type re-estimation of the composite language model parameters.

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“…These models, which are subclasses of Bayesian networks, significantly reduce the high complexity of Bayesian networks by restricting the target to labeled trees or labeled ordered trees [19]. The extension of HTMM to PSTMM for trees is equivalent to that of the hidden Markov model (HMM) [3,17] to the probabilistic context free grammars (PCFGs) [12,14] for sequences (strings). Consequently, the time and space complexity of the learning algorithm of HTMM roughly increases by a factor of the number of states in PSTMM.…”

Section: Introductionmentioning

confidence: 99%

A new efficient probabilistic model for mining labeled ordered trees

Hashimoto

Aoki‐Kinoshita

Ueda

et al. 2006

Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Mining frequent patterns is a general and important issue in data mining. Complex and unstructured (or semi-structured) datasets have appeared in major data mining applications, including text mining, web mining and bioinformatics. Mining patterns from these datasets is the focus of many of the current data mining approaches. We focus on labeled ordered trees, typical datasets of semi-structured data in data mining, and propose a new probabilistic model and its efficient learning scheme for mining labeled ordered trees. The proposed approach significantly improves the time and space complexity of an existing probabilistic modeling for labeled ordered trees, while maintaining its expressive power. We evaluated the performance of the proposed model, comparing it with that of the existing model, using synthetic as well as real datasets from the field of glycobiology. Experimental results showed that the proposed model drastically reduced the computation time of the competing model, keeping the predictive power and avoiding overfitting to the training data. Finally, we assessed our results using the proposed model on real data from a variety of biological viewpoints, verifying known facts in glycobiology.

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The estimation of stochastic context-free grammars using the Inside-Outside algorithm

Cited by 388 publications

References 13 publications

Learning to Perceive Two-Dimensional Displays Using Probabilistic Grammars

Learning to Perceive Two-Dimensional Displays Using Probabilistic Grammars

Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model

A new efficient probabilistic model for mining labeled ordered trees

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