The document discusses natural language processing (NLP) and some of the key challenges involved. It explains that NLP aims to get computers to understand and generate human languages. Understanding written text requires lexical, syntactic, semantic and other knowledge about the language. Ambiguities exist at many levels, from individual words to sentences, and resolving ambiguities is challenging. The document outlines some common sources of ambiguity and methods for addressing them, including part-of-speech tagging and probabilistic parsing. It also discusses models like context-free grammars that are used to represent linguistic knowledge, and algorithms like state space search and dynamic programming that manipulate these models. Finally, it provides an overview of the main steps involved in natural language understanding.
The presentation explains topics on study of language, applications on natural language processing, levels of language analysis, representation and understanding, linguistic background and elements of a simple noun phrase
The presentation explains topics on study of language, applications on natural language processing, levels of language analysis, representation and understanding, linguistic background and elements of a simple noun phrase
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
Natural Language Processing for Games ResearchJose Zagal
Extended version of talk given at GAMNLP Workshop - Kanazawa Japan 2012.
Presents earlier work analyzing game reviews using natural language processing techniques (first previewed at the Game Studies Research Seminar, Tampere Finland 2010)
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
Natural Language Processing for Games ResearchJose Zagal
Extended version of talk given at GAMNLP Workshop - Kanazawa Japan 2012.
Presents earlier work analyzing game reviews using natural language processing techniques (first previewed at the Game Studies Research Seminar, Tampere Finland 2010)
myassignmenthelp is premier service provider for NLP related assignments and projects. Given PPT describes processes involved in NLP programming.so whenever you need help in any work related to natural language processing feel free to get in touch with us.
This presentation educates you about AI - Natural Language Processing, Components of NLP (NLU and NLG), Difficulties in NLU and NLP Terminology and steps of NLP.
For more topics stay tuned with Learnbay.
The best known natural language processing tool is GPT-3, from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this “language models,” and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics, but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model, which enabled it to perform even more advanced tasks like programming and solving high school–level math problems. The latest version, called InstructGPT, has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google’s latest model shows further impressive breakthroughs on language and reasoning.
For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft’s subsidiary GitHub and is capable of creating a basic video game simply by typing instructions. This transformative capability was already expected to change the nature of how programmers do their jobs, but models continue to improve — the latest from Google’s DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions.
Models like GPT-3 are considered to be foundation models — an emerging AI research area — which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI’s DALL·E 2, which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. Module
13
Natural Language
Processing
Version 2 CSE IIT, Kharagpur
2. 13.1 Instructional Objective
• The students should understand the necessity of natural language processing in
building an intelligent system
• Students should understand the difference between natural and formal language and
the difficulty in processing the former
• Students should understand the ambiguities that arise in natural language processing
• Students should understand the language information required like like
o Phonology
o Morphology
o Syntax
o Semantic
o Discourse
o World knowledge
• Students should understand the steps involved in natural language understanding and
generation
• The student should be familiar with basic language processing operations like
o Morphological analysis
o Parts-of-Speech tagging
o Lexical processing
o Semantic processing
o Knowledge representation
At the end of this lesson the student should be able to do the following:
• Design the processing steps required for a NLP task
• Implement the processing techniques.
Version 2 CSE IIT, Kharagpur
3. Lesson
40
Issues in NLP
Version 2 CSE IIT, Kharagpur
4. 13.1 Natural Language Processing
Natural Language Processing (NLP) is the process of computer analysis of input provided
in a human language (natural language), and conversion of this input into a useful form of
representation.
The field of NLP is primarily concerned with getting computers to perform useful and
interesting tasks with human languages. The field of NLP is secondarily concerned with
helping us come to a better understanding of human language.
• The input/output of a NLP system can be:
– written text
– speech
• We will mostly concerned with written text (not speech).
• To process written text, we need:
– lexical, syntactic, semantic knowledge about the language
– discourse information, real world knowledge
• To process spoken language, we need everything required to process written text,
plus the challenges of speech recognition and speech synthesis.
There are two components of NLP.
• Natural Language Understanding
– Mapping the given input in the natural language into a useful
representation.
– Different level of analysis required:
morphological analysis,
syntactic analysis,
semantic analysis,
discourse analysis, …
• Natural Language Generation
– Producing output in the natural language from some internal
representation.
– Different level of synthesis required:
deep planning (what to say),
syntactic generation
• NL Understanding is much harder than NL Generation. But, still both of them are
hard.
The difficulty in NL understanding arises from the following facts:
• Natural language is extremely rich in form and structure, and very ambiguous.
– How to represent meaning,
– Which structures map to which meaning structures.
• One input can mean many different things. Ambiguity can be at different levels.
Version 2 CSE IIT, Kharagpur
5. – Lexical (word level) ambiguity -- different meanings of words
– Syntactic ambiguity -- different ways to parse the sentence
– Interpreting partial information -- how to interpret pronouns
– Contextual information -- context of the sentence may affect the meaning
of that sentence.
• Many input can mean the same thing.
• Interaction among components of the input is not clear.
The following language related information are useful in NLP:
• Phonology – concerns how words are related to the sounds that realize them.
• Morphology – concerns how words are constructed from more basic meaning
units called morphemes. A morpheme is the primitive unit of meaning in a
language.
• Syntax – concerns how can be put together to form correct sentences and
determines what structural role each word plays in the sentence and what phrases
are subparts of other phrases.
• Semantics – concerns what words mean and how these meaning combine in
sentences to form sentence meaning. The study of context-independent meaning.
• Pragmatics – concerns how sentences are used in different situations and how
use affects the interpretation of the sentence.
• Discourse – concerns how the immediately preceding sentences affect the
interpretation of the next sentence. For example, interpreting pronouns and
interpreting the temporal aspects of the information.
• World Knowledge – includes general knowledge about the world. What each
language user must know about the other’s beliefs and goals.
13.1.1 Ambiguity
I made her duck.
• How many different interpretations does this sentence have?
• What are the reasons for the ambiguity?
• The categories of knowledge of language can be thought of as ambiguity
resolving components.
• How can each ambiguous piece be resolved?
• Does speech input make the sentence even more ambiguous?
– Yes – deciding word boundaries
• Some interpretations of : I made her duck.
Version 2 CSE IIT, Kharagpur
6. 1. I cooked duck for her.
2. I cooked duck belonging to her.
3. I created a toy duck which she owns.
4. I caused her to quickly lower her head or body.
5. I used magic and turned her into a duck.
• duck – morphologically and syntactically ambiguous:
noun or verb.
• her – syntactically ambiguous: dative or possessive.
• make – semantically ambiguous: cook or create.
• make – syntactically ambiguous:
– Transitive – takes a direct object. => 2
– Di-transitive – takes two objects. => 5
– Takes a direct object and a verb. => 4
Ambiguities are resolved using the following methods.
• models and algorithms are introduced to resolve ambiguities at different levels.
• part-of-speech tagging -- Deciding whether duck is verb or noun.
• word-sense disambiguation -- Deciding whether make is create or cook.
• lexical disambiguation -- Resolution of part-of-speech and word-sense
ambiguities are two important kinds of lexical disambiguation.
• syntactic ambiguity -- her duck is an example of syntactic ambiguity, and can be
addressed by probabilistic parsing.
13.1.2 Models to represent Linguistic Knowledge
• We will use certain formalisms (models) to represent the required linguistic
knowledge.
• State Machines -- FSAs, FSTs, HMMs, ATNs, RTNs
• Formal Rule Systems -- Context Free Grammars, Unification Grammars,
Probabilistic CFGs.
• Logic-based Formalisms -- first order predicate logic, some higher order logic.
• Models of Uncertainty -- Bayesian probability theory.
13.1.3 Algorithms to Manipulate Linguistic Knowledge
• We will use algorithms to manipulate the models of linguistic knowledge to
produce the desired behavior.
• Most of the algorithms we will study are transducers and parsers.
– These algorithms construct some structure based on their input.
• Since the language is ambiguous at all levels,
these algorithms are never simple processes.
• Categories of most algorithms that will be used can fall into following categories.
– state space search
– dynamic programming
Version 2 CSE IIT, Kharagpur
7. 13.2 Natural Language Understanding
The steps in natural language understanding are as follows:
Words
Morphological Analysis
Morphologically analyzed words (another step: POS tagging)
Syntactic Analysis
Syntactic Structure
Semantic Analysis
Context-independent meaning representation
Discourse Processing
Final meaning representation
Version 2 CSE IIT, Kharagpur