scholarly journals Extended finite state models of language

1996 ◽  
Vol 2 (4) ◽  
pp. 287-290 ◽  
Author(s):  
ANDRÁS KORNAI
Keyword(s):  
Artificial Intelligence ◽  
Text Analysis ◽  
Language Modeling ◽  
Context Sensitive ◽  
The Past ◽  
Finite State ◽  
Wide Availability ◽  
State Models ◽  
Finite State Models ◽  
Context Free

In spite of the wide availability of more powerful (context free, mildly context sensitive, and even Turing-equivalent) formalisms, the bulk of the applied work on language and sublanguage modeling, especially for the purposes of recognition and topic search, is still performed by various finite state methods. In fact, the use of such methods in research labs as well as in applied work actually increased in the past five years. To bring together those developing and using extended finite state methods to text analysis, speech/OCR language modeling, and related CL and NLP tasks with those in AI and CS interested in analyzing and possibly extending the domain of finite state algorithms, a workshop was held in August 1996 in Budapest as part of the European Conference on Artificial Intelligence (ECAI'96).

Extended Finite State Models of Language (review)

Language ◽  
2001 ◽  
Vol 77 (3) ◽  
pp. 610-610
Author(s):  
Michael A. Covington
Keyword(s):  
Finite State ◽  
State Models ◽  
Finite State Models

scholarly journals Statistical compression of protein sequences and inference of marginal probability landscapes over competing alignments using finite state models and Dirichlet priors

2019 ◽  
Vol 35 (14) ◽  
pp. i360-i369
Author(s):  
Dinithi Sumanaweera ◽  
Lloyd Allison ◽  
Arun S Konagurthu
Keyword(s):  
Ad Hoc ◽  
Inductive Reasoning ◽  
Protein Sequences ◽  
Information Criterion ◽  
Supplementary Information ◽  
Marginal Probability ◽  
Source Program ◽  
Finite State ◽  
State Models ◽  
Finite State Models

AbstractThe information criterion of minimum message length (MML) provides a powerful statistical framework for inductive reasoning from observed data. We apply MML to the problem of protein sequence comparison using finite state models with Dirichlet distributions. The resulting framework allows us to supersede the ad hoc cost functions commonly used in the field, by systematically addressing the problem of arbitrariness in alignment parameters, and the disconnect between substitution scores and gap costs. Furthermore, our framework enables the generation of marginal probability landscapes over all possible alignment hypotheses, with potential to facilitate the users to simultaneously rationalize and assess competing alignment relationships between protein sequences, beyond simply reporting a single (best) alignment. We demonstrate the performance of our program on benchmarks containing distantly related protein sequences.Availability and implementationThe open-source program supporting this work is available from: http://lcb.infotech.monash.edu.au/seqmmligner.Supplementary informationSupplementary data are available at Bioinformatics online.

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