Grammar Generation and Optimization from Multiple Inputs

Human being uses multiple modes like speech, text, facial expression, hand gesture, showing picture etc. for communication in between them. The use of this ways for communication makes human communication more simple and fast. In previous years several techniques are used to bring the human computer interaction more closely. It costs more for development and maintenance of Multimodal grammar in integrating and understanding input in multimodal interfaces i.e. using multiple input ways. This leads to improve and investigate more robust algorithm. The proposed system generates the grammar from multiple inputs called as multimodal grammar and evaluates grammar description length. Furthermore, to optimize the multimodal grammar proposed system uses learning operators which improves grammar description DOI: 10.17762/ijritcc2321-8169.15016

Search-Based Evolution of XML Schemas

The use of schemas makes an XML-based application more reliable, since they contribute to avoid failures by defining the specific format for the data that the application manipulates. In practice, when an application evolves, new requirements for the data may be established, raising the need of schema evolution. In some cases the generation of a schema is necessary, if such schema does not exist. To reduce maintenance and reengineering costs, automatic evolution of schemas is very desirable. However, there are no algorithms to satisfactorily solve the problem. To help in this task, this paper introduces a search-based approach that explores the correspondence between schemas and context-free grammars. The approach is supported by a tool, named EXS. Our tool implements algorithms of grammatical inference based on LL(1) Parsing. If a grammar (that corresponds to a schema) is given and a new word (XML document) is provided, the EXS system infers the new grammar that: i) continues to generate the same words as before and ii) generates the new word, by modifying the original grammar. If no initial grammar is available, EXS is also capable of generating a grammar from scratch from a set of samples

Unsupervised Statistical Learning of Context-free Grammar

In this paper, we address the problem of inducing (weighted) context-free grammar (WCFG) on data given. The induction is performed by using a new model of grammatical inference, i.e., weighted Grammar-based Classifier System (wGCS). wGCS derives from learning classifier systems and searches grammar structure using a genetic algorithm and covering. Weights of rules are estimated by using a novelty Inside-Outside Contrastive Estimation algorithm. The proposed method employs direct negative evidence and learns WCFG both form positive and negative samples. Results of experiments on three synthetic context-free languages show that wGCS is competitive with other statistical-based method for unsupervised CFG learning

Unsupervised Syntactic Structure Induction in Natural Language Processing

This work addresses unsupervised chunking as a task for syntactic structure induction, which could help understand the linguistic structures of human languages especially, low-resource languages. In chunking, words of a sentence are grouped together into different phrases (also known as chunks) in a non-hierarchical fashion. Understanding text fundamentally requires finding noun and verb phrases, which makes unsupervised chunking an important step in several real-world applications. In this thesis, we establish several baselines and discuss our three-step knowledge transfer approach for unsupervised chunking. In the first step, we take advantage of state-of-the-art unsupervised parsers, and in the second, we heuristically induce chunk labels from them. We propose a simple heuristic that does not require any supervision of annotated grammar and generates reasonable (albeit noisy) chunks. In the third step, we design a hierarchical recurrent neural network (HRNN) that learns from these pseudo ground-truth labels. The HRNN explicitly models the composition of words into chunks and smooths out the noise from heuristically induced labels. Our HRNN a) maintains both word-level and phrase-level representations and b) explicitly handles the chunking decisions by providing autoregressiveness at each step. Furthermore, we make a case for exploring the self-supervised learning objectives for unsupervised chunking. Finally, we discuss our attempt to transfer knowledge from chunking back to parsing in an unsupervised setting. We conduct comprehensive experiments on three datasets: CoNLL-2000 (English), CoNLL-2003 (German), and the English Web Treebank. Results show that our HRNN improves upon the teacher model (Compound PCFG) in terms of both phrase F1 and tag accuracy. Our HRNN can smooth out the noise from induced chunk labels and accurately capture the chunking patterns. We evaluate different chunking heuristics and show that maximal left-branching performs the best, reinforcing the fact that left-branching structures indicate closely related words. We also present rigorous analysis on the HRNN's architecture and discuss the performance of vanilla recurrent neural networks

Inferência de gramática formais livres de contexto utilizando computação evolucionária com aplicação em bioinformática

Grammatical inference deals with the task of learning a classifier that can recognize a particular pattern in a set of examples. In this work, a new grammatical inference model based on a variant of Genetic Programming is proposed. In this approach, an individual is a list of structured trees representing their productions. Ordinary genetic operators are modified so as to bias the search and two new operators are proposed. The first one, called Incremental Learning, is able to recognize, based on examples, which productions are missing. The second, called Expansion is able to provide the diversity necessary to achieve convergence. In a suite of experiments performed, the proposed model successfully inferred six regular grammars and two context-free grammars: parentheses and palindromes with four letters, including the disjunct one. Results achieved were better than those obtained by recently published algorithms. Nowadays, grammatical inference has been applied to problems of recognition of biological sequences of DNA. In this work, two problems of this class were addressed: recognition of promoters and splice junction detection. In the former, the proposed model obtained results better than other published approaches. In the latter, the proposed model showed promising results. The model was extended to support fuzzy grammars, namely the fuzzy fractional grammars. Furthermore, an appropriate method of estimation of the values of the production's membership function is also proposed. Results obtained in the identification of splice junctions shows the utility of the fuzzy inference model proposed.A inferência gramatical lida com o problema de aprender um classificador capaz de reconhecer determinada construção ou característica em um conjunto qualquer de exemplos. Neste trabalho, um modelo de inferência gramatical baseado em uma variante de Programação Genética é proposto. A representação de cada indivíduo é baseada em uma lista ligada de árvores representando o conjunto de produções da gramática. A atuação dos operadores genéticos é feita de forma heurística. Além disto, dois novos operadores genéticos são apresentados. O primeiro, denominado Aprendizagem Incremental, é capaz de reconhecer, com base em exemplos, quais regras de produção estão faltando. O segundo, denominado Expansão, é capaz de prover a diversidade necessária. Em experimentos efetuados, o modelo proposto inferiu com sucesso seis gramáticas regulares e duas gramáticas livres de contexto: parênteses e palíndromos de quatro letras, tanto o comum quanto o disjunto, sendo superior a abordagens recentes. Atualmente, modelos de inferência gramatical têm sido aplicados a problemas de reconhecimento de sequências biológicas de DNA. Neste trabalho, dois problemas de identificação de padrão foram abordados: reconhecimento de promotores e splice-junction. Para o primeiro, o modelo proposto obteve resultado superior a outras abordagens. Para o segundo, o modelo proposto apresentou bons resultados. O modelo foi estendido para o uso de gramáticas fuzzy, mais especificamente, as gramáticas fuzzy fracionárias. Para tal, um método de estimação adequado dos valores da função de pertinência das produções da gramática é proposto. Os resultados obtidos na identificação de splice-junctions comprovam a utilidade do modelo de inferência gramatical fuzzy proposto

A Mixed-Response Intelligent Tutoring System Based on Learning from Demonstration

Intelligent Tutoring Systems (ITS) have a significant educational impact on student's learning. However, researchers report time intensive interaction is needed between ITS developers and domain-experts to gather and represent domain knowledge. The challenge is augmented when the target domain is ill-defined. The primary problem resides in often using traditional approaches for gathering domain and tutoring experts' knowledge at design time and conventional methods for knowledge representation built for well-defined domains. Similar to evolving knowledge acquisition approaches used in other fields, we replace this restricted view of ITS knowledge learning merely at design time with an incremental approach that continues training the ITS during run time. We investigate a gradual knowledge learning approach through continuous instructor-student demonstrations. We present a Mixed-response Intelligent Tutoring System based on Learning from Demonstration that gathers and represents knowledge at run time. Furthermore, we implement two knowledge representation methods (Weighted Markov Models and Weighted Context Free Grammars) and corresponding algorithms for building domain and tutoring knowledge-bases at run time. We use students' solutions to cybersecurity exercises as the primary data source for our initial framework testing. Five experiments were conducted using various granularity levels for data representation, multiple datasets differing in content and size, and multiple experts to evaluate framework performance. Using our WCFG-based knowledge representation method in conjunction with a finer data representation granularity level, the implemented framework reached 97% effectiveness in providing correct feedback. The ITS demonstrated consistency when applied to multiple datasets and experts. Furthermore, on average, only 1.4 hours were needed by instructors to build the knowledge-base and required tutorial actions per exercise. Finally, the ITS framework showed suitable and consistent performance when applied to a second domain. These results imply that ITS domain models for ill-defined domains can be gradually constructed, yet generate successful results with minimal effort from instructors and framework developers. We demonstrate that, in addition to providing an effective tutoring performance, an ITS framework can offer: scalability in data magnitude, efficiency in reducing human effort required for building a confident knowledge-base, metacognition in inferring its current knowledge, robustness in handling different pedagogical and tutoring criteria, and portability for multiple domain use

SEQUIN: a grammar inference framework for analyzing malicious system behavior

Open access articleTargeted attacks on IT systems are a rising threat to the confidentiality of sensitive data and the availability of critical systems. The emergence of Advanced Persistent Threats (APTs) made it paramount to fully understand the particulars of such attacks in order to improve or devise effective defense mechanisms. Grammar inference paired with visual analytics (VA) techniques offers a powerful foundation for the automated extraction of behavioral patterns from sequential event traces. To facilitate the interpretation and analysis of APTs, we present SEQUIN, a grammar inference system based on the Sequitur compression algorithm that constructs a context-free grammar (CFG) from string-based input data. In addition to recursive rule extraction, we expanded the procedure through automated assessment routines capable of dealing with multiple input sources and types. This automated assessment enables the accurate identification of interesting frequent or anomalous patterns in sequential corpora of arbitrary quantity and origin. On the formal side, we extended the CFG with attributes that help describe the extracted (malicious) actions. Discovery-focused pattern visualization of the output is provided by our dedicated KAMAS VA prototype

Parsing Schemata

Parsing schemata provide a general framework for specication, analysis and comparison of (sequential and/or parallel) parsing algorithms. A grammar specifies implicitly what the valid parses of a sentence are; a parsing algorithm specifies explicitly how to compute these. Parsing schemata form a well-defined level of abstraction in between grammars and parsing algorithms. A parsing schema specifies the types of intermediate results that can be computed by a parser, and the rules that allow to expand a given set of such results with new results. A parsing schema does not specify the data structures, control structures, and (in case of parallel processing)\ud communication structures that are to be used by a parser.\ud Part I, Exposition, gives a general introduction to the ideas that are worked out in the following parts.\ud Part II, Foundation, unfolds a mathematical theory of parsing schemata. Different kinds of relations between parsing schemata are formally introduced and illustrated with examples drawn from the parsing literature.\ud Part III, Application, discusses a series of applications of parsing schemata.\ud - Feature percolation in unification grammar parsing can be described in an elegant, legible notation.\ud - Because of the absence of algorithmic detail, parsing schemata can be used to get a formal grip on highly complicated algorithms. We give substance to this claim by means of a thorough analysis of Left-Corner and Head-Corner chart parsing.\ud - As an example of structural similarity of parsers, despite differences in form and appearance, we show that the underlying parsing schemata of Earley's algorithm and Tomita's algorithm are virtually identical. Using this structural correspondence we can obtain a novel parallel parser by cross-fertilizing a parallel Earley parser with Tomita's graph-structured stack.\ud - Parsing schemata can be implemented straightforwardly by boolean circuits. This means that, in principle, parsing schemata can be coded directly into hardware.\ud Part IV, Perspective, discusses the prospects for natural language parsing applications and draws some conclusions. An important observation is that the theoretical and practical part of the book reinforce each other. The proposed framework is abstract enough to allow a thorough mathematical treatment and practical enough to allow rewriting a variety of real parsing algorithms (i.e. seriously proposed in the literature, not toy examples)\ud in a clear and coherent way