Syntax Parsing: Implementation Using Grammar-Rules for English Language

Collaborative writing tools and Natural language processing are plays a vital role in learning networks. These tools are involved with the dealings among computer systems and human languages. It processes the data through syntax analysis, parsing and lexical analysis, and etc. Syntax analysis is used for syntactic parsing deals with the syntactic structure of a sentence. The collaborative writing tools and natural language processing applications are used for verb tense prediction and it it encodes the temporal order of activities in a sentence. Recognizing the syntactic structure is beneficial in identifying the means of a sentence. The model in this paper is introduced to predict verb tense based on lexical and syntactic features. This model works on English articles, every article will be split to sentences using the tokenization process. Every token in sentences will be analyzed and model will parse the sentences the use of tenses algorithms that represent grammar rules of the English language. This model is given precise accuracy when it is examined on articles/ stories.

Natural Language (NL) is an essential part of ourlife. Humans use language for communication. NL is a prevailing tool used by the humans to convey the information. Natural Language Understanding (NLU) is a major challenge in Natural Language Processing (NLP). NLP is a part of Artificial Intelligence (AI). NLP provides a significant tool for communication. It attempts to produces noise free data and conversion of noise to text. NLU is having different levels. This paper presents the issue with respect to one of the level such as syntax analysis. To provide a solution for syntax analysis, dynamic fuzzy parser is designed and implemented to parse the English input sentences. Traditional approach of parsing is enhanced by applying fuzzy logic. This helps to know the syntactic correctness of the sentence. Penns tree bank parts of speech tags are used for the Parts of Speech Tagger (POS). POS tagger assigns the parts of speech tags for the input English sentence. Then these tags of the words are parsed using the grammar rules. Finally the result is displayed to represent the number of words parsed in a sentence with its associated fuzzy membership value. This parser produces Precision value of 1(100%), Recall value of 0.92 (92%) and F-measure value of 0.9583 for the sample of 50 correct and 50 incorrect sentences.

Natural language processing (NLP) is a branch of artificial intelligence, and is a computational technique which analyzes and synthesizes the natural language written and spoken by humans. The human brain can recognize natural language instantly, but for a machine to understand it, this raw data needs to be processed. Computer applications which work for varying structures of languages can be built using NLP. This process will take unstructured data (human language) as input, and after processing, it will be converted to computer-readable languages. This complex process must follow various attributes and the grammatical structure of natural language. NLP performs syntactic and semantic analysis to identify data patterns hidden in natural language. Syntactic analysis, also called parsing, is a phase whose purpose is to draw the exact meaning of an NLP statement. Formal grammar rules of the underlying language are considered while processing the input. The main aim of parsing is to ensure that the string symbols in natural language comply with the grammar rules. The Graph of Words model is an approach that can be used as a better alternative to the Bag of Words model for text processing. A graph representation of the input documents focuses on word independence by considering the order of words and the distance between relevant words.

Recognition Textual Entailment is a fundamental research direction in natural language processing. However, due to the complexity of sentence structure and the multiple parts of speech of words, it affects the model's deep understanding of sentences. From a linguistic perspective, different parts of speech have different understandings of sentences. We believe that a complete sentence consists of two parts: sentence structure and syntactic structure. In response to this issue, this article proposes a recognition textual entailment model that integrates attention mechanism and part of speech tagging. NLTK (Natural Language Toolkit) is used to annotate the part of speech of sentences, and the part of speech information is interactively fused with the sentence features extracted by the Conformer model. After part of speech tagging, the model can better grasp the syntactic structure of sentences. Experiments have shown that the above method combines sentence structure and syntactic structure, which can effectively enhance the model's understanding of deep semantics and improve its accuracy compared to classical models.

Rapid information and communicationtechnology advancements have prompted widespread interest in natural language processing (NLP) applications. This has led to the development of a plethora of NLP resources. However, several obstacles stand in the way of creating reliable NLP systems that can process natural languages effectively and efficiently. Part of speech (POS) tagging is one such technology;it assigns labels to sentences or phrases inside a paragraph based on where they appear. Researchers have made great strides in POS tagging, but there is always room for improvement, especially in decreasing false positives and correctly categorizing new words. It's also important to remember that there is sure to be some confusion if you tag phrases with manypossible interpretations depending on the surrounding material. In order to effectively identify words in a particular phrase across a paragraph, POS taggers based on “deep learning (DL) and machine learning (ML)” have recently been deployed as viable solutions.

The Malay language is an Austronesian language spoken in most countries in the South East Asia region that includes Malaysia, Indonesia, Singapore, Brunei and Thailand. Traditional linguistics is well developed for Malay but there are very limited resources and tools that are available or made accessible for computer linguistic analysis of Malay language. Assigning part of speech (POS) to running words in a sentence for Malay language is one of the pipeline processes in Natural Language Processing (NLP) tasks and it is not well investigated. This paper outlines an approach to perform the Part of Speech (POS) tagging for Malay text articles. We apply a simple Rule-based Part of Speech (RPOS) tagger to perform the tagging operation on Malay text articles. POS tagging can be described as a task of performing automatic annotation of syntactic categories for each word in a text document. A rule-based POS tagger generally involves a POS tag dictionary and a set of rules in order to identify the words that are considered parts of speech. In this paper, we propose a framework that applies Malay affixing rules to identify the Malay POS tag and the relation between words in order to select the best POS tag for words that have two or more valid POS tags. The results show that the performance accuracy of the ruled-based POS tagger is higher compared to a statistical POS tagger. This indicates that the proposed RPOS tagger is able to predict any unknown word’s POS at some promising accuracy.

Natural Language Processing (NLP) involves the use of algorithms and models and various computational techniques to analyze, process, and generate natural language data, including speech and text. NLP helps computers interact with humans in a more natural way, which has become increasingly important as more humancomputer interactions take place. NLP allows machines to process and analyze voluminous unstructured data, including social media posts, newspaper articles, reviews from customers, emails, and others. It helps organizations extract insights, automate tasks, and improve decision-making by enabling machines to understand and generate human-like language. A linguistic background is essential for understanding NLP. Linguistic theories and models help in developing NLU systems, as NLP specialists need to understand the structure and rules of language. NLU systems are organized into different components, including language modelling, parsing, and semantic analysis. NLU systems may be assessed through the use of metrics that includes measures like precision and recall, as well as indicators that convey meaningful information that include F1 score and others. Semantics and knowledge representation are central to NLU, as they involve understanding the meaning of words and sentences and representing this information in a way that machines can use. Approaches to knowledge representation include semantic networks, ontologies, and vector embeddings. Language modelling is an essential step in NLP that sees usage in applications like speech recognition, text generation, and text completion and also in areas such as machine translation. Ambiguity Resolution remains a major challenge in NLP, as language is often ambiguous and context-dependent. Some common applications of NLP include sentiment analysis, chatbots, virtual assistants, machine translation, speech recognition, text classification, text summarization, and information extraction. In this chapter, we show the applicability of a popular unsupervised learning technique, viz., clustering through K-Means. The efficiency provided by the K-Means algorithm can be improved through the use of an optimization loop. The prospects for NLP are promising, with an increasing demand for AI-powered language technologies in various industries, including healthcare, finance, and e-commerce. There is also a growing need for ethical and responsible AI systems that are transparent and accountable.

The POS (part of speech) tagging is the basic work in natural language processing. The tagging precision will have an important effect on the result of latter process, such as syntax analysis. In this paper, a Chinese POS tagger based on the maximum entropy model is presented, which trains from a large corpus annotated with Chinese POS tags and assigns the best tag sequence to the Chinese sentence to be annotated. In this model, all the features that are useful to predicate the POS tags are mined to make the model closer to the real case. In addition, for the problem of overfitting, a smoothing method and a POS dictionary are maintained to reduce the model's dependence to training data and improve the efficiency of the search process. Open test results shows that the Chinese POS tagging with this method can achieve an accuracy of 96.8%.

Case or agr. Just as abstract Case and agr are cover terms for syntactic features with often idiosyncratic morphological realization, thematic roles seem most useful simply as syntactic designations for positions that enter into argument and adjunct relations. This is a good book, not just for its critique of theta theory, but also for the positive contribution of further development of the Decompositional Theory. Although the book is a revised dissertation, it doesn't read like one: the style is engaging and philosophical, and the exemplification is detailed and clear. And while it does not cover every thematic-role-based proposal ever made, the book is nevertheless to be recommended to anyone who has puzzled over the content of thematic roles. [Edwin Battistella, University of Alabama in Birmingham] Psychology of language: An introduction to sentence and discourse processes. By Murray Singer. Hillsdale, NJ: Lawrence Erlbaum, 1990. Pages xi, 308. $24.95. Recent years have seen the emergence of an essentially interdisciplinary, cognitive-science approach to natural language processing, with some of the most fruitful work focusing on discourse structure and processing. Singer's book is intended as an introductory graduate textbook, though it is also suitable as a desk reference for the linguist, cognitive psychologist, or computer scientist with an interest in natural language processing. An introductory graduate textbook must accomplish several purposes: (i) familiarize students with key theoretical concepts; (ii) refer them to the fundamental literature in which those concepts are discussed; (iii) provide them with exemplars that teach them how to apply the concepts insightfully to the data; and (iv) inculcate a sense of relevance that enables students to read professional articles and evaluate their theoretical significance. S's book is probably above average by these standards. Key concepts are clearly if succinctly explained, fundamental articles are cited in each of the relevant disciplines, and examples are frequent and detailed enough to satisfy the most exacting linguist. The book's weaknesses are those endemic to any introductory text: it emphasizes consensus, spending more time summarizing established results than exploring open issues and controversies, and the passages which explain key concepts are often terse, so that the instructor may sometimes wish to assign source articles as background reading. Overall, the book appears to be a valuable introductory textbook for a course in psycholinguistics or discourse processing. It is divided into ten chapters, as follows: Ch. 1, 'Introduction'; Ch. 2, 'Language and meaning: Representing and remembering discourse'; Ch. 3, 'Syntax and parsing processes'; Ch. 4, The role of knowledge in language comprehension'; Ch. 5, 'Understanding coherent discourse'; Ch. 6, Theme'; Ch. 7, 'Inference processes'; Ch. 8, 'Understanding stories'; Ch. 9, 'Question answering and sentence verification'; and Ch. 10, 'Natural language understanding by computers—and people'. One of the noteworthy aspects of the book is its thorough integration of psycholinguistic data with the concepts of discourse analysis, including coherence, the givennew contract, thematic structure, and scripts. [Paul Deane, University of Central Florida.] An essay on grammar-parser relations. By Jan van de Koot. Dordrecht: Foris, 1990. Pp. xii, 152. Paper $24.50. In this monograph, K investigates the possible relations between grammars and parsers. Any theory of the mind that postulates separate 240 LANGUAGE, VOLUME 68, NUMBER 1 (1992) levels of representations raises the question of what relations are possible, justified and beneficial among the several levels. Two such theories are Marr's theory of vision and Chomskyan generative grammar, with their separation of competence from performance. The book under review belongs to the research line of computer modelling of performance started by Parsifal (Mitchell Marcus, A theory of syntactic recognition for natural language, Cambridge, MA: MIT Press, 1980) in the Government-Binding framework. The monograph starts with a critical introduction to the relevant issues. Through extensive quotations from Marr's work (David Marr, Vision, San Francisco, CA: Freeman, 1982) K presents the methodological justification for multileveled theories of cognition. He then proceeds to explore, in the more limited domain of the language faculty, the necessary features of a psychologically real theory of grammar. The arguments that led some scholars to consider transformational grammar not psychologically real are presented and rebutted by the introduction of a more articulated framework for evaluation, which takes into account the separateness of competence grammar, runtime grammar, parsing technique, and evaluation metrics. Although nothing new is added to the debate here, the main issues of a discussion that spans a decade are reviewed clearly and concisely. Ch. 2 is an overview of several proposals in the framework of deterministic parsing. Most notably, the first such parser, Parsifal, is presented in detail. Arguing against some recent criticism of Parsifal's use of look-ahead, K claims that the use of look-ahead to resolve local ambiguities is empirically correct if coupled with the assumption that lexical-semantic information can be used during syntactic processing, when it is needed for disambiguation. K also reviews the formal properties of Parsifal, drawing on other works in the literature to argue that determinism provides a functional explanation of WH-islands at most, and that it is neutral with respect to grammatical formalism. Ch. 3 explores the relation between the Barriers theory (Noam Chomsky, Barriers. Cambridge, MA: MIT Press, 1986) and deterministic parsing. The parsing platform is Parsifal, with alterations and augmentations that link it more tightly to the grammar and make it more accurate empirically. An algorithm for chain formation is proposed here. K discusses its integration in the existing parser and its application to some linguistic facts, such as violations of the Subject and Adjunct Conditions and parasitic gaps. Some interesting ideas are put forth in this chapter, although more extensive exemplification and, if not a real implementation, at least lower-level specifications of the algorithms would have substantiated the proposal more convincingly. The book ends with a number of theoretical investigations of the possible contribution of the theory of performance to the theory of competence. In general, the reader interested in these issues will find in K's book an accurate and critical review of the main issues in the tradition of deterministic parsing, in itself a needed and useful contribution. Because the book is not intended as a review, however, more space should have been devoted to the author's new proposal; interesting ideas, such as those related to the algorithms for chain formation, are not given enough space and detailed description. The book would have also benefited from more careful editing and typesetting. [Paola Merlo. University ofMaryland.] Politically speaking: The pragmatic analysis of political language. By John Wilson. (Language in Society series.) Basil Blackwell, 1990. Pp. 203. Cloth $45.95, paper $16.95. Politically speaking offers an interesting and frequently amusing look at political speech from the perspective of linguistic pragmatics. Unlike Robin Lakoffs Talking power (New York: Basic Books, 1990), it does not investigate political power or even politics in a generic sense; and, unlike Frederick Bowers's Linguistic aspects oflegislative expression (Vancouver: University of British Columbia Press, 1990). it does not examine statutory language. Rather, Wilson explores politicians' utterances in speeches and interviews. Examples from other cultures are used, but mostly the study deals with recent British and American political figures. A notable feature of the book, W's quotations—from Margaret Thatcher's 'We are a grandmother' to Ronald Reagan's '[in England] a criminal with a gun, even if he was arrested for burglary, was tried for first degree murder and hanged if found

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

Natural Language Processing, one of the most important branches of Artificial Intelligence, has close links with Logic Programming. It is one of the two roots from which Logic Programming developed, and still shares important core concepts with it (e.g. unification). The concepts and techniques used for two of its main tasks, viz. syntax analysis and semantic analysis, are illustrated with concrete examples. For syntax analysis, it is the notations of Definite Clause Grammars and Extraposition Grammars, for semantic analysis, the concepts of Montague Grammar and Generalized Quantifier Theory. Some of these techniques can also be applied to the analysis of formal languages.

Universidade Federal de Sao Carlos (UFSCar), Dept Letras, BR-13565905 Sao Carlos, SP, Brazil

In the age of knowledge, Natural Language Processing (NLP) express its demand by a huge range of utilization. Previously NLP was dealing with statically data. Contemporary time NLP is doing considerably with the corpus, lexicon database, pattern reorganization. Considering Deep Learning (DL) method recognize artificial Neural Network (NN) to nonlinear process, NLP tools become increasingly accurate and efficient that begin a debacle. Multi-Layer Neural Network obtaining the importance of the NLP for its capability including standard speed and resolute output. Hierarchical designs of data operate recurring processing layers to learn and with this arrangement of DL methods manage several practices. In this paper, this resumed striving to reach a review of the tools and the necessary methodology to present a clear understanding of the association of NLP and DL for truly understand in the training. Efficiency and execution both are improved in NLP by Part of speech tagging (POST), Morphological Analysis, Named Entity Recognition (NER), Semantic Role Labeling (SRL), Syntactic Parsing, and Coreference resolution. Artificial Neural Networks (ANN), Time Delay Neural Networks (TDNN), Recurrent Neural Network (RNN), Convolution Neural Networks (CNN), and Long-Short-Term-Memory (LSTM) dealings among Dense Vector (DV), Windows Approach (WA), and Multitask learning (MTL) as a characteristic of Deep Learning. After statically methods, when DL communicate the influence of NLP, the individual form of the NLP process and DL rule collaboration was started a fundamental connection.

With the rapid growth of online content written in the Kurdish language, there is an increasing need to make it machine-readable and processable. Part of speech (POS) tagging is a critical aspect of natural language processing (NLP), playing a significant role in applications such as speech recognition, natural language parsing, information retrieval, and multiword term extraction. This study details the creation of the DASTAN corpus, the first POS-annotated corpus for the Sorani Kurdish dialect. The corpus, containing 74,258 words and thirty-eight tags, employs a hybrid approach utilizing the bigram hidden Markov model in combination with the Kurdish rule-based approach to POS tagging. This approach addresses two key problems that arise with rule-based approaches, namely misclassified words and ambiguity-related unanalyzed words. The proposed approach’s accuracy was assessed by training and testing it on the DASTAN corpus, yielding a 96% accuracy rate. Overall, this study’s findings demonstrate the effectiveness of the proposed hybrid approach and its potential to enhance NLP applications for Sorani Kurdish.

Automatic Parts of Speech(POS) tagging is one of the most fundamental tasks for a language in Natural Language Processing(NLP), which acts as a feature for solving advanced NLP tasks. Named Entity Recognition(NER) is another essential task of NLP for information retrieval. Researchers could not find up to the mark solution yet on these two tasks for Bangla language compared to other languages, for instance, English, Ger-man. Moreover, many solutions heavily depend on handcrafted features that require strong linguistic expertise. As these two sequence labeling tasks are similar, In this work, two different datasets of POS tagging and NER were prepared, and different deep neural network approaches studied for solving these two tasks separately. All of the approaches were end to end and did not need any handcrafted feature like word suffixes or affixes, gazetteers, dictionary. This study came up with an end to end solution using deep neural network-based model consisting of Bi-directional Long short-term memory(BLSTM), Convolutional Neural Network(CNN) and Conditional Random Field(CRF). The proposed model trained on respected datasets achieved an accuracy of 93.86% on POS tagging and a strict f1 score of 0.6285 on NER on prepared datasets, respectively.