Completion Grammars and Completion Automata Revisited

1977 ◽  
Vol 37 ◽  
pp. 19-40
Author(s):  
Luc Steels
Keyword(s):  
Language Processing ◽  
Word Order ◽  
Generative Capacity ◽  
New Class ◽  
Automatic Translation ◽  
Linguistic Model ◽  
Transition Functions ◽  
Finite State ◽  
Formal Properties

An extension of completion grammars is being introduced such that the model now deals with prefix, infix, postfix and post-infix word order patterns. It is shown that this extension does not affect the weak generative capacity of the system, which was known to be of type 2. Also the existing notion of a completion automaton is reworked, mainly to have the distinction in word order be reflected by the operations of the automaton rather than by the transition functions of the underlying finite state machine. In some recent publications (e.g. Steels (1975), Steels and Vermeir (1976), Steels (1976a&b» we have been dealing with a linguistic model known as compZetion grammars. These grammars were designed to cope with a functional viewpoint on language, this means they deal with case structures for language,expressions, instead of phrase structures as do the well-known Chomsky-type grammars. The model of completion grammars was developed in a context of research on language processing and automatic translation. In particular it reflects the current tendency to build semantics directed systems, rather than syntax directed ones. (See for a more detailed discussion on the distinction between the two Wilks (1975) and Winograd (1973). For the use of completion grammars in the design of semantics directed systems, we refer to Steels (1975;1976a&b). What will concern us in this paper is an extension of the model, and a study of the formal properties of these extended systems. Also we will introduce a new class of automata. The paper is organized as follows. First we extend the notion of a completion grammar, we give some intuitive explanations for the extension (1.1.), specify the basic definitions (1.2.) and study its weak generative capacity (1.3.). A second section deals with the automata. Again we start with intuitive explanations (2.1.), give the basic definitions and various examples (2.2.) and finally prove the relation between the grammars and the automata (2.3.).

Processing subject focus across two Spanish varieties

Probus ◽  
2020 ◽  
Vol 32 (1) ◽  
pp. 93-127
Author(s):  
Bradley Hoot ◽  
Tania Leal
Keyword(s):  
Language Processing ◽  
Sentence Processing ◽  
Word Order ◽  
Judgment Task ◽  
Spanish Speakers ◽  
Experimental Results ◽  
Final Position ◽  
Reading Task ◽  
New Information ◽  
Basic Facts

AbstractLinguists have keenly studied the realization of focus – the part of the sentence introducing new information – because it involves the interaction of different linguistic modules. Syntacticians have argued that Spanish uses word order for information-structural purposes, marking focused constituents via rightmost movement. However, recent studies have challenged this claim. To contribute sentence-processing evidence, we conducted a self-paced reading task and a judgment task with Mexican and Catalonian Spanish speakers. We found that movement to final position can signal focus in Spanish, in contrast to the aforementioned work. We contextualize our results within the literature, identifying three basic facts that theories of Spanish focus and theories of language processing should explain, and advance a fourth: that mismatches in information-structural expectations can induce processing delays. Finally, we propose that some differences in the existing experimental results may stem from methodological differences.

scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

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 Liraglutide in Adults with Type 2 Diabetes: Global Perspective on Safety, Efficacy and Patient Preference

2011 ◽  
Vol 4 ◽  
pp. CMED.S5976 ◽  
Author(s):  
Daisuke Yabe ◽  
Yutaka Seino
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trials ◽  
Glucagon Secretion ◽  
Japanese Patients ◽  
Global Perspective ◽  
Daily Injection ◽  
Efficacy And Safety ◽  
New Class ◽  
Glucagon Like Peptide 1

Incretin-based therapies have been gaining much attention recently as a new class of therapeutics for type 2 diabetes worldwide. Among them, glucagon-like peptide-1 receptor agonist liraglutide has been rapidly increasing its global usage. Once daily injection of liraglutide significantly ameliorates glycemic control in patients with type 2 diabetes by enhancing insulin secretion and suppressing glucagon secretion glucose-dependently. Liraglutide delays gastric emptying and suppresses food intakes, both of which contribute to glucose lowering and weight reduction. Efficacy and safety of liraglutide in management of type 2 diabetes have been well documented in several key clinical trials such as series of phase 3 Liraglutide Effect and Action in Diabetes (LEAD) trials, and the liraglutide-versus-sitagliptin trial. Recent two trials dealing with monotherapy and sulfonylurea combination therapy on Japanese patients with type 2 diabetes furthermore indicate liraglutide's effectiveness in non-obese diabetes. In this review, we summarize results from such clinical trials, and discuss efficacy and safety of liraglutide in management of type 2 diabetes in various countries, along with a pitfall of liraglutide usage in real clinical setting.

scholarly journals The quantification of type-2 prudence in asset allocation by the trustees of a retirement fund

2016 ◽  
Vol 10 (2) ◽  
pp. 169-202
Author(s):  
Robert J. Thomson ◽  
Taryn L. Reddy
Keyword(s):  
Risk Aversion ◽  
Asset Allocation ◽  
Weighted Average ◽  
Expected Utility Theory ◽  
Model Parameters ◽  
Defined Benefit ◽  
Defined Contribution ◽  
New Class ◽  
Retirement Funds

AbstractIn this paper, consideration is given to the normative use of expected-utility theory for the purposes of asset allocation by the trustees of retirement funds. A distinction is drawn between “type-1 prudence”, which relates to deliberate conservatism on the part of actuaries in the setting of assumptions and the determination of model parameters, and “type-2 prudence”, which relates to the risk aversion of the trustees. The intention of the research was to quantify type-2 prudence for the purposes of asset allocation, both for defined-contribution (DC) and defined-benefit (DB) funds. The authors propose new definitions of the objective variables used as the argument of the utility function: one for DC funds and another for DB funds. A new class of utility functions, referred to as the “weighted average relative risk aversion” class is proposed. Practicalities of implementation are discussed. Illustrative results of the application of the method are presented, and it is shown that the proposed approach resolves the paradox of counter-intuitive results found in the literature regarding the sensitivity of the optimal asset allocation to the funding level of a DB fund.

scholarly journals Sodium-glucose co-transporter 2 inhibition as a mitochondrial therapy for atrial fibrillation in patients with diabetes?

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Salva R. Yurista ◽  
Herman H. W. Silljé ◽  
Michiel Rienstra ◽  
Rudolf A. de Boer ◽  
B. Daan Westenbrink
Keyword(s):  
Atrial Fibrillation ◽  
Mitochondrial Dysfunction ◽  
High Fat Diet ◽  
Antiarrhythmic Drugs ◽  
New Class ◽  
Increased Risk ◽  
Patients With Diabetes ◽  
Series Of Experiments ◽  
New Evidence

AbstractWhile patients with type 2 diabetes mellitus (T2DM) are at increased risk to develop atrial fibrillation (AF), the mechanistic link between T2DM and AF-susceptibility remains unclear. Common co-morbidities of T2DM, particularly hypertension, may drive AF in the setting of T2DM. But direct mechanisms may also explain this relation, at least in part. In this regard, recent evidence suggests that mitochondrial dysfunction drives structural, electrical and contractile remodelling of atrial tissue in patients T2DM. Mitochondrial dysfunction may therefore be the mechanistic link between T2DM and AF and could also serve as a therapeutic target. An elegant series of experiments published in Cardiovascular Diabetology provide compelling new evidence to support this hypothesis. Using a model of high fat diet (HFD) and low-dose streptozotocin (STZ) injection, Shao et al. provide data that demonstrate a direct association between mitochondrial dysfunction and the susceptibility to develop AF. But the authors also demonstrated that the sodium-glucose co-transporter 2 inhibitors (SGLT2i) empagliflozin has the capacity to restore mitochondrial function, ameliorate electrical and structural remodelling and prevent AF. These findings provide a new horizon in which mitochondrial targeted therapies could serve as a new class of antiarrhythmic drugs.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

Finite-State Technology

2012 ◽  
Author(s):  
Lauri Karttunen
Keyword(s):  
Language Processing ◽  
Regular Language ◽  
Morphological Analysis ◽  
Finite State Automata ◽  
Regular Expressions ◽  
Finite State Transducers ◽  
Finite State ◽  
Shallow Parsing ◽  
Basic Concepts ◽  
State Language

The article introduces the basic concepts of finite-state language processing: regular languages and relations, finite-state automata, and regular expressions. Many basic steps in language processing, ranging from tokenization, to phonological and morphological analysis, disambiguation, spelling correction, and shallow parsing, can be performed efficiently by means of finite-state transducers. The article discusses examples of finite-state languages and relations. Finite-state networks can represent only a subset of all possible languages and relations; that is, only some languages are finite-state languages. Furthermore, this article introduces two types of complex regular expressions that have many linguistic applications, restriction and replacement. Finally, the article discusses the properties of finite-state automata. The three important properties of networks are: that they are epsilon free, deterministic, and minimal. If a network encodes a regular language and if it is epsilon free, deterministic, and minimal, the network is guaranteed to be the best encoding for that language.

Transducer parsing of free and frozen sentences

1996 ◽  
Vol 2 (4) ◽  
pp. 345-350
Author(s):  
EMMANUEL ROCHE
Keyword(s):  
Language Processing ◽  
Finite State Transducers ◽  
Finite State ◽  
State Models ◽  
Finite State Models ◽  
The Cost ◽  
Lesser Evil

In language processing, finite state models are not a lesser evil that bring simplicity and efficiency at the cost of accuracy. On the contrary, they provide a very natural framework to describe complex linguistic phenomena. We present here one aspect of parsing with finite state transducers and show that this technique can be applied to complex linguistic situations.

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