#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...

[ithaca-my-beloved]

Can't believe I had to miss my morphology lecture because comp sci has no concept of timetables

[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

[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]

POEM

"POEM"

Pity The Lilly Of My Friend

The Mire Was Wet With The

Skies Earth S Descend

Lyca D In A Little A

---

Dacre And Mutual Fear

And They Know I Vanish Innocent

Hands Full Of The Voices Appear

Gifts Coined Gold Struggling Millisent

[tumnikkeimatome]

最新のM365 Excel in PythonでNLTKとワードクラウドによるテキスト分析と可視化を実現する方法

Excel Python環境の初期設定 Microsoft 365のExcelでPythonを使用するには、事前の環境設定が必要です。 管理コンソールでPython機能を有効化し、必要なライブラリをインストールします。 # 必要なライブラリのインポート import nltk from wordcloud import WordCloud import matplotlib.pyplot as plt import pandas as pd # NLTKの必要なデータをダウンロード nltk.download('punkt') nltk.download('stopwords') テキストデータの前処理とNLTK解析の実践手法 Natural Language Toolkit…

[zarazyana]

How Can You Build an Effective AI Agent for Customer Support?

In today’s digital age, customer support has transformed from a reactive to a proactive function, evolving into a pivotal part of the customer experience. Traditional support methods are being replaced by AI agents—intelligent systems designed to interact with users, resolve queries, and deliver 24/7 assistance. Developing an effective AI agent development for customer support can enhance user satisfaction, streamline operations, and reduce costs. But how do you create an AI agent that’s both capable and customer-friendly?

This guide will walk you through the essential steps, technologies, and best practices to develop an AI-driven customer support agent that aligns with modern business needs.

1. Understanding the Role of AI Agents in Customer Support

AI agents for customer support are software programs powered by Artificial Intelligence, specifically designed to understand customer queries, retrieve information, and resolve issues autonomously. These agents can range from basic chatbots that follow pre-set rules to sophisticated virtual assistants equipped with Natural Language Processing (NLP) and Machine Learning (ML) capabilities that learn and improve over time.

Key benefits of AI customer support agents include:

24/7 Availability: AI agents can work around the clock, catering to users in different time zones.

Scalability: They can handle multiple queries simultaneously, reducing wait times.

Cost Efficiency: AI agents lower operational costs by minimizing human intervention for routine queries.

Enhanced Customer Satisfaction: Quick, accurate responses improve customer experience.

2. Defining Objectives and Scope for Your AI Agent

Before diving into development, define your agent’s role within your customer support strategy. Understanding your objectives and setting clear expectations will help guide the development process.

Consider these questions:

What are the primary functions of the AI agent? (e.g., answering FAQs, troubleshooting, processing returns)

What type of user interactions will it handle? (text, voice, or a combination)

What level of complexity is required? A rule-based agent may suffice for simple inquiries, whereas a learning-based agent might be needed for nuanced interactions.

How will the AI agent integrate with existing support channels? Ensure it aligns with your CRM and support ticket systems.

Having clear goals will help shape the architecture, technology stack, and training data you’ll need.

3. Choosing the Right Technology Stack

Building an effective AI agent requires a mix of core technologies that enable understanding, processing, and responding to customer inputs:

a. Natural Language Processing (NLP)

NLP allows AI agents to understand and interpret human language, the backbone of conversational AI. With NLP, the agent can analyze user intent, sentiment, and even nuances in language.

Popular NLP tools and libraries:

OpenAI’s GPT (Generative Pre-trained Transformer)

Google’s Dialogflow

IBM Watson Assistant

Microsoft Azure Bot Service

SpaCy and NLTK (Natural Language Toolkit) for more customized solutions

b. Machine Learning (ML) and Deep Learning (DL)

ML and DL algorithms allow your AI agent to improve over time. Through training, the agent learns patterns in customer interactions, enabling it to handle increasingly complex queries and provide better responses.

Key ML tools:

TensorFlow and Keras: Ideal for training custom ML models.

PyTorch: Popular for complex neural networks and NLP applications.

Scikit-Learn: Great for basic machine learning models and data processing.

c. Automated Speech Recognition (ASR) and Text-to-Speech (TTS)

For voice-based agents, ASR converts spoken language into text, while TTS transforms responses into natural-sounding speech.

Popular ASR and TTS tools:

Google’s Text-to-Speech API

Amazon Polly

Microsoft Azure Speech API

d. Integration with CRM and Backend Systems

An effective AI agent for customer support should integrate seamlessly with existing systems, such as:

Customer Relationship Management (CRM) platforms (e.g., Salesforce, HubSpot) for storing customer data and support tickets.

Ticketing Systems (e.g., Zendesk, Freshdesk) to automate the process of logging, escalating, and resolving support issues.

Knowledge Bases: Having access to product information and FAQs helps the AI agent deliver accurate responses.

4. Designing the User Experience (UX) for Your AI Agent

An AI agent’s success is significantly influenced by its usability and the overall user experience it offers. A well-designed interface and response structure are crucial for customer engagement.

UX Best Practices:

Conversational Flow: Plan out common user journeys, scripting responses for various types of inquiries and guiding users toward solutions.

Personalized Interactions: Use customer data to personalize responses, greeting users by name, or remembering past interactions to provide relevant answers.

Clear Escalation Options: If the AI agent cannot resolve an issue, it should smoothly transfer the query to a human agent. Clear messages about escalation build trust.

Natural Tone and Language: Avoid robotic phrasing. The more conversational the tone, the more users will feel comfortable interacting with the agent.

5. Data Collection and Training the AI Agent

The effectiveness of your AI agent relies on its training data. Training an agent involves providing it with enough examples of customer queries, responses, and possible variations.

Data Sources for Training:

Historical Chat Transcripts: Gather past conversations between customers and support agents to create realistic training data.

FAQs and Knowledge Base Articles: Ensure the agent is trained on the most common customer inquiries.

User Feedback and Surveys: Use feedback to improve the agent’s responses, focusing on areas where it may be lacking or misunderstood queries.

Key Considerations in Training:

Supervised Learning: For high-quality responses, use labeled data where customer queries are matched with correct responses.

Continuous Learning: Establish mechanisms for ongoing learning so the AI agent can adapt based on recent interactions and emerging customer trends.

Handling Variations in Language: Train the AI agent to recognize different ways customers may phrase questions, including slang, typos, and colloquial language.

6. Testing the AI Agent

Once trained, rigorous testing is crucial before deploying your AI agent to ensure accuracy and a seamless user experience.

Types of Testing:

Functionality Testing: Verify that the AI agent performs as expected, responding correctly to both common and complex queries.

Usability Testing: Involve real users to test the agent’s responses and conversational flow, identifying potential areas for improvement.

Performance Testing: Evaluate the agent’s ability to handle a high volume of interactions without lags, especially during peak times.

Fallback Mechanism Testing: Confirm that the agent properly escalates issues it cannot resolve to human agents and communicates clearly when it reaches its limitations.

7. Deployment and Integration

Once tested, deploy the AI agent to your desired customer support channels. Integration is key to providing a seamless experience, enabling the agent to access data and update systems as needed.

Common Deployment Channels:

Website: Embed the AI agent directly into your website for live chat support.

Mobile App: Integrate the AI agent into your mobile app to enhance customer experience on the go.

Messaging Platforms: Deploy on platforms like WhatsApp, Facebook Messenger, or Slack to meet customers on their preferred channels.

Voice-Enabled Devices: If applicable, make the AI agent available through voice-activated assistants like Amazon Alexa or Google Assistant.

Integration Checklist:

Ensure the agent can retrieve and update customer data in real-time.

Test interactions across multiple platforms to ensure consistency.

Implement logging mechanisms to track performance and user feedback.

8. Monitoring and Optimization

Deployment is only the beginning. Monitoring the AI agent’s performance and continually optimizing it based on user interactions and feedback is essential for long-term success.

Key Metrics to Track:

Customer Satisfaction (CSAT): Measure customer satisfaction to gauge the agent’s effectiveness.

Response Accuracy: Regularly review the agent’s accuracy to ensure it provides correct responses.

Resolution Rate: Track the percentage of issues resolved by the AI agent versus those escalated to human agents.

Engagement Rate: Assess how many users interact with the AI agent and the duration of these interactions to understand engagement.

Ongoing Optimization Strategies:

Feedback Loops: Use customer feedback to refine the agent’s responses and improve accuracy.

Regular Model Retraining: Update the agent’s training data to keep up with evolving customer needs and product changes.

A/B Testing: Experiment with variations in response tone, conversation flow, and escalation options to improve user satisfaction.

9. Future Considerations: Evolving Your AI Agent

AI technology is constantly evolving, which means there are opportunities to enhance your AI agent over time:

Emotional Intelligence: Future developments in affective computing could enable AI agents to detect and respond to customer emotions, making interactions more personalized.

Proactive Support: Equip your AI agent to provide proactive assistance by notifying users about service outages, order updates, or renewal reminders.

Multilingual Support: As global reach expands, consider implementing multilingual capabilities to cater to non-English speaking customers.

Conclusion

Building an effective AI agent for customer support involves strategic planning, choosing the right technologies, designing for user experience, and ongoing improvement. By carefully defining your objectives, training the agent on quality data, and integrating it with your customer support ecosystem, you can create an AI-powered agent that enhances customer satisfaction, reduces operational costs, and scales effortlessly with your business. With the right approach, an AI agent can be an invaluable asset to your customer support strategy, delivering exceptional service and fostering lasting customer loyalty.

[web-scraping-tutorial-blog]

The most useful programming language in the field of artificial intelligence (AI)

Artificial intelligence (AI) is a technology that refers to the ability of computer systems to mimic human intelligence and perform various tasks. Artificial intelligence (AI) is a computer-based technology that allows humans to think in a single way. The Python language has become the language of choice in the field of artificial intelligence due to its simplicity, ease of learning, and flexibility.

This article describes the use of Python in artificial intelligence.

Machine learning

Python has a rich set of libraries and frameworks. Python libraries commonly used in machine learning include Numpy,pandas and scikit-learn.

Furthermore, there are many libraries that compile the functions necessary for machine learning, which increases the amount of resources that can be used, leading to reduced development costs and increased efficiency.

Natural language processing (NLP)

Python is also widely used in natural language processing. There is a wide range of NLP libraries and frameworks such as NLTK and SpaCy that can be used to analyze and process large amounts of text data.

Computer vision

Python has a wide range of computer vision libraries and frameworks, such as OpenCV and his Scikit-image, that help users perform operations such as image enhancement, target detection, and segmentation to draw useful conclusions. Additionally, Python can be used to build applications such as robot vision systems and drone vision systems.

Finally, it should be pointed out that the applications of Python in the field of artificial intelligence are not limited to fields such as machine learning, natural language processing, and computer vision. Python can also be used in other areas such as the Internet of Things, blockchain, and financial technology. If you want to work in the field of artificial intelligence, learning Python is very necessary.