#NLTK

[pythonprogrammingsnippets]

python iterative monte carlo search for text generation using nltk

You are playing a game and you want to win. But you don't know what move to make next, because you don't know what the other player will do. So, you decide to try different moves randomly and see what happens. You repeat this process again and again, each time learning from the result of the move you made. This is called iterative Monte Carlo search. It's like making random moves in a game and learning from the outcome each time until you find the best move to win.

Iterative Monte Carlo search is a technique used in AI to explore a large space of possible solutions to find the best ones. It can be applied to semantic synonym finding by randomly selecting synonyms, generating sentences, and analyzing their context to refine the selection.

# an iterative monte carlo search example using nltk # https://pythonprogrammingsnippets.tumblr.com import random from nltk.corpus import wordnet # Define a function to get the synonyms of a word using wordnet def get_synonyms(word): synonyms = [] for syn in wordnet.synsets(word): for l in syn.lemmas(): if '_' not in l.name(): synonyms.append(l.name()) return list(set(synonyms)) # Define a function to get a random variant of a word def get_random_variant(word): synonyms = get_synonyms(word) if len(synonyms) == 0: return word else: return random.choice(synonyms) # Define a function to get the score of a candidate sentence def get_score(candidate): return len(candidate) # Define a function to perform one iteration of the monte carlo search def monte_carlo_search(candidate): variants = [get_random_variant(word) for word in candidate.split()] max_candidate = ' '.join(variants) max_score = get_score(max_candidate) for i in range(100): variants = [get_random_variant(word) for word in candidate.split()] candidate = ' '.join(variants) score = get_score(candidate) if score > max_score: max_score = score max_candidate = candidate return max_candidate initial_candidate = "This is an example sentence." # Perform 10 iterations of the monte carlo search for i in range(10): initial_candidate = monte_carlo_search(initial_candidate) print(initial_candidate)

output:

This manufacture Associate_in_Nursing theoretical_account sentence. This fabricate Associate_in_Nursing theoretical_account sentence. This construct Associate_in_Nursing theoretical_account sentence. This cathode-ray_oscilloscope Associate_in_Nursing counteract sentence. This collapse Associate_in_Nursing computed_axial_tomography sentence. This waste_one's_time Associate_in_Nursing gossip sentence. This magnetic_inclination Associate_in_Nursing temptingness sentence. This magnetic_inclination Associate_in_Nursing conjure sentence. This magnetic_inclination Associate_in_Nursing controversy sentence. This inclination Associate_in_Nursing magnetic_inclination sentence.

[robotpoetry]

POURING

"Pouring"

Pity In The Southern Clime Slow

Do Their Nest Merrily Morneault

Father Sold Me How They Buis

Where The Beetle Goes His Work Says

---

Eat Hoarse With Joy In Die

Moon Arise In The Lily White Di

And The Heat Till She Imo

Upon A Thorn And Wroe

[theharrymanback]

El arte de escribir bien (y algunas estadísticas al respecto)

El arte de escribir bien (y algunas estadísticas al respecto)

Desde hace un tiempo, muy a menudo me sorprendo a mí mismo maravillado frente a un texto. Pero no porque la historia que cuenta sea impresionante o me esté dando información súper interesante. A medida que mi lista de lecturas ha ido creciendo, también lo ha hecho mi gozo al encontrar pasajes particularmente bien escritos. Además, durante los últimos años he invertido muchas horas intentando…

[josegremarquez]

El concepto de los diccionarios de sentimientos y cómo son fundamentales en el análisis de sentimientos.

¿Qué son los diccionarios de sentimientos y cómo funcionan? Imagina un diccionario, pero en lugar de definir palabras, clasifica las palabras según la emoción que expresan. Estos son los diccionarios de sentimientos. Son como una especie de “tesauro emocional” que asigna a cada palabra una puntuación que indica si es positiva, negativa o neutral. ¿Cómo funcionan? Lexicón: Contienen una extensa…

[soumenatta]

In this tutorial, we will explore how to perform sentiment analysis using Python with three popular libraries — NLTK, TextBlob, and VADER.

[semperintrepida]

Not me out here writing a program to log into my AO3 account and perform a natural language sentiment analysis of the comments in my inbox to identify trolls without having to read their garbage...

[techinfotrends]

Want to make NLP tasks a breeze? Explore how NLTK streamlines text analysis in Python, making it easier to extract valuable insights from your data. Discover more https://bit.ly/487hj9L

[evilplumpie]

part of speech tagging? oh man sorry i though u meant piece of shit tagging

[tapejob]

.

[pythonprogrammingsnippets]

python keyword extraction using nltk wordnet

import re # include wordnet.morphy from nltk.corpus import wordnet # https://pythonprogrammingsnippets.tumblr.com/ def get_non_plural(word): # return the non-plural form of a word # if word is not empty if word != "": # get the non-plural form non_plural = wordnet.morphy(word, wordnet.NOUN) # if non_plural is not empty if non_plural != None: # return the non-plural form # print(word, "->", non_plural) return non_plural # if word is empty or non_plural is empty return word def get_root_word(word): # return the root word of a word # if word is not empty if word != "": word = get_non_plural(word) # get the root word root_word = wordnet.morphy(word) # if root_word is not empty if root_word != None: # return the root word # print(word, "->", root_word) word = root_word # if word is empty or root_word is empty return word def process_keywords(keywords): ret_k = [] for k in keywords: # replace all characters that are not letters, spaces, or apostrophes with a space k = re.sub(r"[^a-zA-Z' ]", " ", k) # if there is more than one whitespace in a row, replace it # with a single whitespace k = re.sub(r"\s+", " ", k) # remove leading and trailing whitespace k = k.strip() k = k.lower() # if k has more than one word, split it into words and add each word # back to keywords if " " in k: ret_k.append(k) # we still want the original keyword k = k.split(" ") for k2 in k: #if not is_adjective(k2): ret_k.append(get_root_word(k2)) ret_k.append(k2.strip()) else: # if not is_adjective(k): ret_k.append(get_root_word(k)) ret_k.append(k.strip()) # unique ret_k = list(set(ret_k)) # remove empty strings ret_k = [k for k in ret_k if k != ""] # remove all words that are less than 3 characters ret_k = [k for k in ret_k if len(k) >= 3] # remove words like 'and', 'or', 'the', etc. ret_k = [k for k in ret_k if k not in ["and", "or", "the", "a", "an", "of", "to", "in", "on", "at", "for", "with", "from", "by", "as", "into", "like", "through", "after", "over", "between", "out", "against", "during", "without", "before", "under", "around", "among", "throughout", "despite", "towards", "upon", "concerning", "of", "to", "in", "on", "at", "for", "with", "from", "by", "as", "into", "like", "through", "after", "over", "between", "out", "against", "during", "without", "before", "under", "around", "among", "throughout", "despite", "towards", "upon", "concerning", "this", "that", "these", "those", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "will", "would", "shall", "should", "can", "could", "may", "might", "must", "ought", "i", "me", "my", "mine", "we", "us", "our", "ours", "you", "your", "yours", "he", "him", "his", "she", "her", "hers", "it", "its", "they", "them", "their", "theirs", "what", "which", "who", "whom", "whose", "this", "that", "these", "those", "myself", "yourself", "himself", "herself", "itself", "ourselves", "yourselves", "themselves", "whoever", "whatever", "whomever", "whichever", "whichever" ]] return ret_k def extract_keywords(paragraph): if " " in paragraph: return paragraph.split(" ") return [paragraph]

example usage:

the_string = "Jims House of Judo and Karate is a martial arts school in the heart of downtown San Francisco. We offer classes in Judo, Karate, and Jiu Jitsu. We also offer private lessons and group classes. We have a great staff of instructors who are all black belts. We have been in business for over 20 years. We are located at 123 Main Street." keywords = process_keywords(extract_keywords(the_string)) print(keywords)

output:

# output: ['jims', 'instructors', 'class', 'lesson', 'all', 'school', 'san', 'martial', 'classes', 'karate', 'great', 'lessons', 'downtown', 'private', 'arts', 'also', 'locate', 'belts', 'business', 'judo', 'years', 'located', 'main', 'street', 'jitsu', 'house', 'offer', 'staff', 'group', 'heart', 'instructor', 'belt', 'black', 'francisco', 'jiu']

[robotpoetry]

LITTLE

"Little"

Lamb So Do Not Thou Prause

I Was Wet With Soft Baus

Sweet Is Not Alone Nor Rejoice

These Flowers While Thou Complainest Now Intervoice

---

Thee The Tiger Tiger Thuma

Moon Lovely Lyca Sleep Fama

Little Bird That In Her Haws

On Earth To Drive Their Son Naus

[pandeypankaj]

Is Python part of the future of programming?

Yes, of course, Python has an extremely bright future in the field of programming. It is gaining more and more fame every day, and its versatility has made the language lead in most areas.

Therefore, some of the major reasons that account for Python playing a leading role in the world of programming are:

Data Science and Machine Learning: Python has extensive libraries for tasks that involve anything to do with data analysis, machine learning modeling, and deep learning. Python's use will only increase with more data-driven decisions being made.

Web Development: Python's Django and Flask have recently gained considerable momentum in web application development. Their simplicity, scalability, and enormous community support ensure their continued relevance.

Scientific Computing: The readability of Python, combined with some powerful libraries like SciPy and SymPy for example, render Python very popular for usage by researchers and engineers into different fields like physics, chemistry, and biology. 

Automation and Scripting: Python is very useful for automation of tasks or bringing up administrations; it also provides a flexible language for Web scraping.

Artificial Intelligence and Natural Language Processing: Python is especially applied in AI and NLP because of the flexibility of this language and the availability of special libraries that include NLTK and spaCy. The range of its applications includes everything from chatbots to sentiment analyses to language translation.

Consequently, Python is a very versatile, well-supported language with a rich ecosystem, which really brightens its future. Further development and adoption in a variety of industries could seal its status as one of the cornerstones of modern computing.

[blogchaindeveloper]

How to Become a Prompt Engineer?

The skill of prompt engineering involves creating and perfecting directives or input language that act as guidelines for AI models like ChatGPT and DALLE-2, enabling them to generate precise and focused outputs. This procedure ensures AI models produce results that fit predetermined standards and parameters smoothly.

Quick engineers meticulously improve the training data that is fed into AI models. Careful and strategic selection and data organization are needed to maximize the data's usefulness for training.

The Key to Timely Engineering

Swift engineering is the same as molding AI models' brains. It entails crafting and honing input text that acts as a compass, assisting AI systems in producing targeted, intended results. Consider it the magic wand that guarantees AI models perfectly align with predetermined standards and parameters.

The Way to Become an Intense Engineer

Step 1: Understanding the NLP Foundations

Start by familiarizing yourself with natural language processing (NLP) principles. This is the real beginning of human-machine communication. Explore ideas such as named entity recognition (identifying names and entities), part-of-speech tagging (identifying word roles), tokenization (dividing text into words), and syntactic parsing (comprehending sentence structure). These essential components establish the basis for interacting with conversational AI systems like ChatGPT.

Step 2: Knowledge of Python

When it comes to NLP and AI, Python is your reliable partner. Learn the fundamentals first:

Variables are data containers.

Data types are classifications like text or numbers

Control flow is the process of making decisions in code.

Functions are repeatable sections of code.

Explore more complex subjects as you develop, such as modules (reusable code you may import), packages (groups of modules), and file handling (reading and writing files). TensorFlow and PyTorch are essential libraries you should familiarize yourself with because they are vital to your rapid engineering journey.

Step 3: Examine NLP Frameworks and Libraries

Use NLTK to begin executing Natural Language Processing (NLP) operations. It provides a large selection of tools and datasets to get you started. You can then go to spaCy, renowned for its effectiveness in NLP processing using models that have already been trained. Another helpful tool that gives you access to state-of-the-art transformer models like ChatGPT is Hugging Face's Transformers. Then, you may use these tools for real-world tasks like sentiment analysis (finding emotions in text), text classification (classifying text), language production (producing writing), and text preparation (cleaning and organizing text).

Step 4: Examine Transformer Models' Internal Mechanisms

Examine in-depth the construction and functionality of transformer models, such as the one that powers ChatGPT. Learn how the brain processes information (encoder-decoder) and maintains word order (positional encoding) in self-attention. This understanding will enable you to comprehend how ChatGPT responds logically and topically.

Step 5: Utilizing Pre-Trained ChatGPT Models in a Practical Setting

Try out ChatGPT models that are ready to use, such as GPT-2 or GPT-3. Try experimenting with various queries and prompts to learn what they can and cannot accomplish. It's similar to conversing with friends to get to know them better.

Step 6: Adjusting for Particular Uses

Learn how to modify ChatGPT to suit your requirements and tasks. This entails fine-tuning it to excel at specific activities. It will be necessary for you to understand transfer learning, prepare data, and make adjustments to settings. Consider it to make your ChatGPT extremely intelligent for various chat scenarios.

Step 7: Awareness of Bias and Ethical Issues

Respect moral principles as a prompt engineer and be aware of potential biases in AI models. Recognize the significance of ethical AI development and the impact biases can have on model output and training data. Keep abreast of regulations to guarantee AI systems' transparency and fairness.

Step 8: Keep Up with Innovative Research

Stay current on the most recent NLP and AI research. To stay current on the latest methods, models, and ChatGPT research, visit conferences, interact with the AI community, and subscribe to reputable sites.

Step 9: Work Together and Provide Open Source Contributions

Become active in open-source NLP and AI initiatives. Collaborate with other experts and contribute to research projects, libraries, and frameworks that expand ChatGPT's functionality. This cooperative method promotes professional development, a diversity of viewpoints, and real-world experience.

Step 10: Utilize Skills in Practical Projects

Work on real-world NLP and conversational AI projects to solidify your expertise. Seek opportunities to use ChatGPT to tackle real-world issues. Putting together a portfolio of accomplished projects can help you become more proficient with ChatGPT and show prospective employers your capabilities.

The Way to a Lucrative Career

Prompt engineers are in great demand as businesses increasingly use AI to improve customer experiences and processes. AI prompt engineers in the US make an average of roughly $98,000 a year, which makes it a lucrative and fulfilling employment choice.

Opening Doors: The AI Era's Importance of Early Engineering Certification

The architects influencing the behavior of AI models such as ChatGPT are prompt engineers. Obtaining a quick engineer accreditation is more than just a title; it's evidence of your proficiency in influencing AI behavior, guaranteeing precision, and reducing prejudices. 

It shows your dedication to ethically developing AI and using technology sensibly. With the knowledge and abilities to traverse the changing AI world, prompt engineering courses and certifications open doors to intriguing careers and professional advancement. With an AI certification, you can be sure you're at the vanguard of this ongoing change in our world, upholding the values of excellence, transparency, and accountability in AI development.

The path to becoming an AI prompt engineer combines technical expertise with strong communication and problem-solving abilities. If you follow this thorough road map, you'll start an exciting career, positively impacting the rapidly evolving fields of NLP and AI. As you discover ChatGPT's and related AI models' full potential, you'll contribute to the wave of advancement in AI that will lead to countless opportunities in the future.

The Blockchain Council is a reputable association of subject matter experts and enthusiasts dedicated to promoting Blockchain technology, applications, goods, and information for a better society. Blockchain technology is more than just a new, developing technology with significant future potential. Blockchain functions as a distributed ledger, software, financial network, and more.

Companies are switching from their centralized, old working method to this cutting-edge, futuristic technology called "Blockchain" due to its many advantages and characteristics. Additionally, they provide quick engineering certifications that can help you succeed in your AI profession.

[evilplumpie]

in tragic news my literature review is "substantively finished" and now I have to code things and do statistics ugh!!!

[coconutsplit]

Creating a tool that helps manage digital mental space while sifting through content and media is a valuable and challenging project. Here’s a high-level breakdown of how you might approach this:

1. Define the Scope and Features

Digital Mental Space Management:

Focus Mode: Create a feature that blocks or filters out distracting content while focusing on specific tasks.

Break Reminders: Set up reminders for taking regular breaks to avoid burnout.

Content Categorization: Allow users to categorize content into different sections (e.g., work, personal, leisure) to manage their mental space better.

Content Sifting and Filtering:

Keyword Filtering: Implement a keyword-based filtering system to highlight or exclude content based on user preferences.

Sentiment Analysis: Integrate a sentiment analysis tool that can categorize content as positive, negative, or neutral, helping users choose what to engage with.

Source Verification: Develop a feature that cross-references content with reliable sources to flag potential misinformation.

2. Technical Components

Front-End:

UI/UX Design: Design a clean, minimalistic interface focusing on ease of use and reducing cognitive load.

Web Framework: Use frameworks like React or Vue.js for responsive and interactive user interfaces.

Content Display: Implement a dashboard that displays categorized and filtered content in an organized way.

Back-End:

API Integration: Use APIs for content aggregation (e.g., news APIs, social media APIs) and filtering.

Data Storage: Choose a database (e.g., MongoDB, PostgreSQL) to store user preferences, filtered content, and settings.

Authentication: Implement a secure authentication system to manage user accounts and personalized settings.

Content Filtering and Analysis:

Text Processing: Use Python with libraries like NLTK or SpaCy for keyword extraction and sentiment analysis.

Machine Learning: If advanced filtering is needed, train a machine learning model using a dataset of user preferences.

Web Scraping: For content aggregation, you might need web scraping tools like BeautifulSoup or Scrapy (ensure compliance with legal and ethical standards).

3. Development Plan

Phase 1: Core Functionality

Develop a basic UI.

Implement user authentication.

Set up content aggregation and display.

Integrate keyword filtering.

Phase 2: Advanced Features

Add sentiment analysis.

Implement break reminders and focus mode.

Add source verification functionality.

Phase 3: Testing and Iteration

Conduct user testing to gather feedback.

Iterate on the design and features based on user feedback.

Optimize performance and security.

4. Tools and Libraries

Front-End: React, Redux, TailwindCSS/Material-UI

Back-End: Node.js/Express, Django/Flask, MongoDB/PostgreSQL

Content Analysis: Python (NLTK, SpaCy), TensorFlow/PyTorch for ML models

APIs: News API, Twitter API, Facebook Graph API

Deployment: Docker, AWS/GCP/Azure for cloud deployment

5. Considerations for User Well-being

Privacy: Ensure user data is protected and handled with care, possibly offering anonymous or minimal data modes.

Customization: Allow users to customize what types of content they want to filter, what kind of breaks they want, etc.

Transparency: Make the filtering and analysis process transparent, so users understand how their content is being sifted and managed.

This is a comprehensive project that will require careful planning and iteration. Starting small and building up the tool's features over time can help manage the complexity.