Grammar rules in English, Dependency Parsing, Shallow parsing
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The document outlines the rules and structure of grammar in English, defining it as essential for creating well-formed sentences and programming syntax. It discusses various types of grammars, including context-free grammar, constituency grammar, and dependency grammar, along with their components and relationships among words in sentences. Additionally, it explains concepts such as constituency, grammatical relations, and parsing techniques, emphasizing the importance of shallow parsing in natural language processing.
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L-3 BCE OS FINAL.pptKirti Verma This document provides an introduction to operating systems. It discusses what an operating system is, its key functions such as process management, memory management, file management, device management, and security. It describes the evolution of operating systems from early batch systems to modern multiprogramming, time-sharing, and distributed systems. Popular types of operating systems are also outlined, including desktop, server, mobile, and embedded operating systems. Key topics like kernels, system calls, computer architecture, and user interfaces are summarized as well.
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L-1 BCE computer fundamentals final kirti.pptKirti Verma The document defines a computer and describes its key advantages such as speed, accuracy, storage capability, diligence, and versatility. It then discusses some disadvantages like lack of intelligence, dependency on humans, and lack of feelings. The document also provides overviews of several topics related to computing including e-business, bioinformatics, healthcare applications, remote sensing, geographic information systems, meteorology/climatology, and computer gaming. Finally, it describes the fundamental components of a computer including the CPU, memory subsystem, I/O subsystem, and how they are connected via buses. It provides details on registers, instruction format, and the instruction cycle.
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BCE L-3 overview of C++.pptKirti Verma Prof. Kirti Verma is a professor in the Computer Science and Engineering department at LNCT University in Bhopal, India. The document provides the name and department of Prof. Kirti Verma at LNCT University in Bhopal.
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BCE L-1 Programmimg languages.pptx
BCE L-1 Programmimg languages.pptxKirti Verma The document discusses several programming paradigms including imperative, object-oriented, and declarative paradigms. Imperative programming uses procedures and functions to manipulate data, exemplified by languages like C and Pascal. Object-oriented programming revolves around objects and classes, promoting concepts like inheritance and encapsulation in languages such as Java and C++. Declarative programming treats computation as the evaluation of mathematical functions, emphasizing immutability and pure functions in languages like Haskell and Lisp. The document also outlines the six phases of the program development life cycle: problem definition, problem analysis, algorithm development, coding and documentation, testing and debugging, and maintenance.
BCE L-1 networking fundamentals 111.pptx
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BCE L-3omputer security Basics.pptx
BCE L-3omputer security Basics.pptxKirti Verma Computer security involves protecting computing systems and data from theft or damage. It ensures confidentiality, integrity, and availability of data. Common computer security threats include unauthorized access, hackers, viruses, and social engineering. Antivirus software, firewalls, and keeping systems updated help enhance security. Laws also aim to prevent cybercrimes like privacy violations, identity theft, and electronic funds transfer fraud. Overall computer security requires technical safeguards and vigilance from users.
L 5 Numpy final learning and Coding
L 5 Numpy final learning and CodingKirti Verma NumPy is a Python library that provides multidimensional arrays and matrices for numerical computing along with high-level mathematical functions to operate on these arrays. NumPy arrays can represent vectors, matrices, images, and tensors. NumPy allows fast numerical computing by taking advantage of optimized low-level C/C++ implementations and parallel computing on multicore processors. Common operations like element-wise array arithmetic and universal functions are much faster with NumPy than with native Python.
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L 2 Introduction to Data science Kirti Verma The document appears to be a presentation by Kirti Verma, who holds the positions of AP and CSE at LNCTE. The presentation does not provide any other details about its content or purpose within the given text.
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Grammar rules in English, Dependency Parsing, Shallow parsing
- 1. Grammar Rules for English
- 2. Grammar is defined as the rules for forming well-structured sentences. Grammar also plays an essential role in describing the syntactic structure of well- formed programs, like denoting the syntactical rules used for conversation in natural languages.
- 3. Mathematically, a grammar G can be written as a 4-tuple (N, T, S, P) where: ◦ N orVN = set of non-terminal symbols or variables. ◦ T or ∑ = set of terminal symbols. ◦ S = Start symbol where S N ∈ ◦ P = Production rules for Terminals as well as Non- terminals. ◦ It has the form α→βα→β, where and are α β strings on VN ∑ ∪ VN ∑, and at least one symbol ∪ of belongs toVN α
- 4. Components of Syntax Syntax also refers to the way words are arranged together. Constituency: Groups of words may behave as a single unit or phrase - A constituent, for example, like a Noun phrase. Grammatical relations: These are the formalization of ideas from traditional grammar. Examples include - subjects and objects. Subcategorization and dependency relations: These are the relations between words and phrases, for example, aVerb followed by an infinitive verb. Regular languages and part of speech: Refers to the way words are arranged together but cannot support easily. Examples are Constituency, Grammatical relations, and Subcategorization and dependency relations. Syntactic categories and their common denotations in NLP: np - noun phrase, vp - verb phrase, s - sentence, det - determiner (article), n - noun, tv - transitive verb (takes an object), iv - intransitive verb, prep - preposition, pp - prepositional phrase, adj - adjective
- 5. Types of Grammars Context Free Grammar Constituency Grammar (CG) Dependency Grammar (DG)
- 6. Context-free grammar Context-free grammar consists of a set of rules expressing how symbols of the language can be grouped and ordered together and a lexicon of words and symbols. CFG consists of a finite set of grammar rules having the following four components Set of Non-terminals: It is represented byV.The non-terminals are syntactic variables that denote the sets of strings, which helps in defining the language that is generated with the help of grammar. Set ofTerminals: It is also known as tokens and represented by . Strings are Σ formed with the help of the basic symbols of terminals. Set of Productions: It is represented by P.The set gives an idea about how the terminals and nonterminals can be combined. Every production consists of the following components:Non-terminals,Arrow,Terminals (the sequence of terminals). The left side of production is called non-terminals while the right side of production is called terminals. Start Symbol: The production begins from the start symbol. It is represented by symbol S. Non-terminal symbols are always designated as start symbols.
- 7. Constituency Grammar It is also known as Phrase structure grammar. Furthermore, it is called constituency Grammar as it is based on the constituency relation. It is the opposite of dependency grammar.
- 8. The constituents can be any word, group of words or phrases in Constituency Grammar.The goal of constituency grammar is to organize any sentence into its constituents using their properties. Characteristic properties of constituency grammar and constituency relation: ◦ All the related frameworks view the sentence structure in terms of constituency relation. ◦ To derive the constituency relation, we take the help of subject-predicate division of Latin as well as Greek grammar. ◦ In constituency grammar, we study the clause structure in terms of noun phrase NP and verb phraseVP.
- 9. For Example, constituency grammar can organize any sentence into its three constituents- a subject, a context, and an object. Sentence: <subject> <context> < <subject>The horses /The dogs /They <context> are running / are barking / are eating <object> in the park / happily / since the morning
- 10. Dependency Grammar Dependency Grammar states that words of a sentence are dependent upon other words of the sentence.These Words are connected by directed links in dependency grammar.The verb is considered the center of the clause structure. Dependency Grammar organizes the words of a sentence according to their dependencies. Every other syntactic unit is connected to the verb in terms of a directed link. These syntactic units are called dependencies. ◦ One of the words in a sentence behaves as a root, and all the other words except that word itself are linked directly or indirectly with the root using their dependencies. ◦ These dependencies represent relationships among the words in a sentence, and dependency grammar is used to infer the structure and semantic dependencies between the words.
- 14. Step 6: Dependency parsing Next comes dependency parsing which is mainly used to find out how all the words in a sentence are related to each other.To find the dependency, we can build a tree and assign a single word as a parent word.The main verb in the sentence will act as the root node. The edges in a dependency tree represent grammatical relationships. These relationships define words’ roles in a sentence, such as subject, object, modifier, or adverbial. Subject-Verb Relationship: In a sentence like “She sings,” the word “She” depends on “sings” as the subject of the verb.
- 15. Modifier-Head Relationship: In the sentence “The big cat,” “big” modifies “cat,” creating a modifier-head relationship. Direct Object-Verb Relationship: In “She eats apples,” “apples” is the direct object that depends on the verb “eats.” Adverbial-Verb Relationship: In “He sings well,” “well” modifies the verb “sings” and forms an adverbial-verb relationship.
- 16. DependencyTag Description acl clausal modifier of a noun (adnominal clause) acl:relcl relative clause modifier advcl adverbial clause modifier advmod adverbial modifier advmod:emph emphasizing phrase, intensifier advmod:lmod locative adverbial modifier amod adjectival modifier appos appositional modifier aux auxiliary aux:move passive auxiliary case case-marking cc coordinating conjunction cc:preconj preconjunct ccomp clausal complement clf classifier compound compound conj conjunct cop copula csubj clausal topic csubj:move clausal passive topic dep unspecified dependency det determiner det:numgov рrоnоminаl quаntifier gоverning the саse оf the nоun det:nummod r n min l qu ntifier agreeing with the se f the n un р о о а а са о о det:poss possessive determiner discourse discourse ingredient dislocated dislocated parts expl expletive expl:impers impersonal expletive expl:move reflexive pronoun utilized in reflexive passive expl:pv reflexive clitic with an inherently reflexive verb mounted mounted multiword expression flat flat multiword expression flat:overseas overseas phrases flat:title names goeswith goes with iobj oblique object checklist checklist mark marker nmod nominal modifier nmod:poss possessive nominal modifier nmod:tmod temporal modifier
- 18. Shallow parsing It is also known as chunking, is a type of natural language processing (NLP) technique that aims to identify and extract meaningful phrases or chunks from a sentence. Unlike full parsing, which involves analyzing the grammatical structure of a sentence, shallow parsing focuses on identifying individual phrases or constituents, such as noun phrases, verb phrases, and prepositional phrases. Shallow parsing is an essential component of many NLP tasks, including information extraction, text classification, and sentiment analysis.
- 19. Full parsing involves analyzing the entire grammatical structure of a sentence, which can be computationally intensive and time-consuming. Shallow parsing, on the other hand, involves identifying and extracting only the most important phrases or constituents, making it faster and more efficient than full parsing. This makes shallow parsing particularly useful for applications that require processing large volumes of text, such as web crawling, document indexing, and machine translation.