scholarly journals Regular relations for temporal propositions

2011 ◽  
Vol 17 (2) ◽  
pp. 163-184 ◽  
Author(s):  
TIM FERNANDO
Keyword(s):  
Natural Language ◽  
Information Content ◽  
Truth Conditions ◽  
Finite State Transducers ◽  
Finite State

AbstractRelations computed by finite-state transducers are applied to interpret temporal propositions in terms of strings representing finite contexts or situations. Carnap–Montague intensions mapping indices to extensions are reformulated as relations between strings that can serve as indices and extensions alike. Strings are related according to information content, temporal span and granularity, the bounds on which reflect the partiality of natural language statements. That partiality shapes not only strings-as-extensions (indicating what statements are about) but also strings-as-indices (underlying truth conditions).

Regular expressions for language engineering

1996 ◽  
Vol 2 (4) ◽  
pp. 305-328 ◽  
Author(s):  
L. KARTTUNEN ◽  
J-P. CHANOD ◽  
G. GREFENSTETTE ◽  
A. SCHILLE
Keyword(s):  
Natural Language ◽  
Regular Expression ◽  
Regular Expressions ◽  
Language Engineering ◽  
Finite State Transducers ◽  
Finite State ◽  
Processing Steps

Many of the processing steps in natural language engineering can be performed using finite state transducers. An optimal way to create such transducers is to compile them from regular expressions. This paper is an introduction to the regular expression calculus, extended with certain operators that have proved very useful in natural language applications ranging from tokenization to light parsing. The examples in the paper illustrate in concrete detail some of these applications.

scholarly journals Composition of weighted finite transducers in MapReduce

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Bilal Elghadyry ◽  
Faissal Ouardi ◽  
Sébastien Verel
Keyword(s):  
Speech Processing ◽  
Large Scale ◽  
Large Scale Data ◽  
Finite State Transducers ◽  
Wide Range ◽  
Finite State ◽  
Common Operation ◽  
Efficient Representation ◽  
Weighted Finite State Transducers ◽  
Np Hardness

AbstractWeighted finite-state transducers have been shown to be a general and efficient representation in many applications such as text and speech processing, computational biology, and machine learning. The composition of weighted finite-state transducers constitutes a fundamental and common operation between these applications. The NP-hardness of the composition computation problem presents a challenge that leads us to devise efficient algorithms on a large scale when considering more than two transducers. This paper describes a parallel computation of weighted finite transducers composition in MapReduce framework. To the best of our knowledge, this paper is the first to tackle this task using MapReduce methods. First, we analyze the communication cost of this problem using Afrati et al. model. Then, we propose three MapReduce methods based respectively on input alphabet mapping, state mapping, and hybrid mapping. Finally, intensive experiments on a wide range of weighted finite-state transducers are conducted to compare the proposed methods and show their efficiency for large-scale data.

FINITELY SUBSEQUENTIAL TRANSDUCERS

2003 ◽  
Vol 14 (06) ◽  
pp. 983-994 ◽  
Author(s):  
CYRIL ALLAUZEN ◽  
MEHRYAR MOHRI
Keyword(s):  
Speech Recognition ◽  
Finite Number ◽  
Efficient Algorithm ◽  
Experimental Results ◽  
Theoretical Formulation ◽  
Large Vocabulary ◽  
Finite State Transducers ◽  
Finite State ◽  
Large Vocabulary Speech Recognition

Finitely subsequential transducers are efficient finite-state transducers with a finite number of final outputs and are used in a variety of applications. Not all transducers admit equivalent finitely subsequential transducers however. We briefly describe an existing generalized determinization algorithm for finitely subsequential transducers and give the first characterization of finitely subsequentiable transducers, transducers that admit equivalent finitely subsequential transducers. Our characterization shows the existence of an efficient algorithm for testing finite subsequentiability. We have fully implemented the generalized determinization algorithm and the algorithm for testing finite subsequentiability. We report experimental results showing that these algorithms are practical in large-vocabulary speech recognition applications. The theoretical formulation of our results is the equivalence of the following three properties for finite-state transducers: determinizability in the sense of the generalized algorithm, finite subsequentiability, and the twins property.

Finite-State Technology

2018 ◽  
Author(s):  
Mans Hulden
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Finite State Machines ◽  
Regular Languages ◽  
Finite State Automata ◽  
State Machines ◽  
Computational Phonology ◽  
Finite State

Finite-state machines—automata and transducers—are ubiquitous in natural-language processing and computational linguistics. This chapter introduces the fundamentals of finite-state automata and transducers, both probabilistic and non-probabilistic, illustrating the technology with example applications and common usage. It also covers the construction of transducers, which correspond to regular relations, and automata, which correspond to regular languages. The technologies introduced are widely employed in natural language processing, computational phonology and morphology in particular, and this is illustrated through common practical use cases.

scholarly journals Negation of Conditionals in Natural Language and Thought

2021 ◽  
Vol 27 (1) ◽  
pp. 46-63
Author(s):  
Gilberto Gomes
Keyword(s):  
Natural Language ◽  
Language Use ◽  
Human Reasoning ◽  
Truth Conditions ◽  
Language And Thought ◽  
Pragmatic Truth ◽  
Natural Language Pragmatics ◽  
Excluded Middle

External negation of conditionals occurs in sentences beginning with ‘It is not true that if’ or similar phrases, and it is not rare in natural language. A conditional may also be denied by another with the same antecedent and opposite consequent. Most often, when the denied conditional is implicative, the denying one is concessive, and vice versa. Here I argue that, in natural language pragmatics, ‘If $A$, $\sim B$’ entails ‘$\sim$(if $A, B$)’, but ‘$\sim$(if $A, B$)’ does not entail ‘If $A$, $\sim B$’. ‘If $A, B$’ and ‘If $A$, $\sim B$’ deny each other, but are contraries, not contradictories. Truth conditions that are relevant in human reasoning and discourse often depend not only on semantic but also on pragmatic factors. Examples are provided showing that sentences having the forms ‘$\sim$(if $A, B$)’ and ‘If $A$, $\sim B$’ may have different pragmatic truth conditions. The principle of Conditional Excluded Middle, therefore, does not apply to natural language use of conditionals. Three squares of opposition provide a representation the aforementioned relations.

scholarly journals An evaluation of conflation accuracy using finite‐state transducers

2006 ◽  
Vol 62 (3) ◽  
pp. 328-349 ◽  
Author(s):  
Carmen Galvez ◽  
Félix de Moya‐Anegón
Keyword(s):  
Finite State Transducers ◽  
Finite State
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