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Learning Machine Learning from Scratch - Getting Started with NLP

Popularity：415 ℃/2024-11-21 13:20:15

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Today we are going to delve into the field of Natural Language Processing (NLP). Natural Language Processing is an important subfield of Artificial Intelligence that focuses on how to enable machines to understand and process human language so that they can perform a variety of tasks such as spell checking, machine translation, and more.

Currently, the application scenarios of natural language processing are quite extensive, and the familiar intelligent assistants such as Siri and Alexa are typical examples. In the next course, we will first implement a basic version of a chatbot, followed by a step-by-step analysis of how to optimize and enhance the intelligent performance of the bot to make it closer to the way humans think.

Well, let's take it from here!

chatbot

As we learn Natural Language Processing (NLP), it will be important to acquire the following skills:

  1. Python 3: As a powerful and easy-to-learn programming language, Python 3 is the language of choice for natural language processing, with a rich set of libraries and frameworks that effectively support a wide range of NLP tasks.
  2. Your favorite Python IDE: Choosing an integrated development environment (IDE) that suits your needs can greatly improve programming efficiency, whether it's PyCharm, Jupyter Notebook, or VS Code, it's crucial to find the tool that works best for you.
  3. TextBlob: This is a library built on top of two popular libraries, the Natural Language Toolkit (NLTK) and Pattern.TextBlob leverages the power of these two libraries to make text analysis and processing easier and more intuitive. It provides a user-friendly API for a variety of natural language processing tasks, including sentiment analysis, text categorization, translation, etc. TextBlob is a great tool for rapid development and prototyping, and is especially suited for projects that need to work with text data.

No-brainer version of chatting

Trying to implement a perfectly functional chatbot at the beginning is obviously unrealistic for us beginners. Therefore, we will take a step-by-step approach by implementing a basic version of the chatbot first, and see if we can build on it with continuous optimization, and eventually move towards the goal of our ideal intelligent bot.

Now, we will proceed to implement one of the simplest chatbots. This bot will interact with the user by responding randomly until the user chooses to end the conversation.

code implementation

import random

# This list contains the random responses (you can add your own or translate them into your own language too)
random_responses = ["That is quite interesting, please tell me more.",
                    "I see. Do go on.",
                    "Why do you say that?",
                    "Funny weather we've been having, isn't it?",
                    "Let's change the subject.",
                    "Did you catch the game last night?"]

print("Hello, I am Marvin, the simple robot.")
print("You can end this conversation at any time by typing 'bye'")
print("After typing each answer, press 'enter'")
print("How are you today?")

while True:
    # wait for the user to enter some text
    user_input = input("> ")
    if user_input.lower() == "bye":
        # if they typed in 'bye' (or even BYE, ByE, byE etc.), break out of the loop
        break
    else:
        response = (random_responses)[0]
    print(response)

print("It was nice talking to you, goodbye!")

Hello, I am Marvin, the simple robot.
You can end this conversation at any time by typing 'bye'
After typing each answer, press 'enter'
How are you today?

I am good thanks
That is quite interesting, please tell me more.

ok, lets talk about music
Why do you say that?

because I like music!
Why do you say that?

bye
It was nice talking to you, goodbye!

In fact, the process is still relatively simple, so there is no need to explain it too much here. However, we can find some interesting issues in it.

First of all, do you think these random responses can "trick" humans into thinking that the robot actually understands them? This is a thought-provoking question that goes to the very nature of human-robot interaction.

Second, in order for the bot to respond more effectively to the user, what capabilities does it need to have? For example, does the bot need to have contextual understanding in order to better track the topic and direction of the conversation?

Moreover, if a robot can really "understand" the meaning of a sentence, does it also need to "memorize" the meaning of the previous sentence in order to maintain consistency and coherence in a conversation?

For most Natural Language Processing (NLP) tasks, programs must decompose text, examine it, and store the results of the processing or cross-reference them with related rules and datasets. These tasks enable programmers to extract information about the meaning, intent, or frequency of terms and words in text.

Next, we'll take a look at some of the issues facing NLP experts. While we may not need to delve into the underpinnings at the moment, it's useful to get a general impression of the challenges. After all, mastering the use of the tool is our primary goal at the moment. Through practice, we will gradually gain experience and explore more complex concepts and techniques in depth.

NLP Common Tasks

In fact, our main goal is simply to analyze and process the text effectively. By understanding these natural language processing tasks, we hope to extract valuable information and draw the conclusions we seek.

  1. tokenization
    • Splitting text into tokens or words requires consideration of punctuation and linguistic features. For example, split the sentence "The cat was sleeping on the windowsill." Split it into tags: ["cat", "on", "windowsill", "on", "sleeping", "."]. .
  2. embedding
    • Converting textual data into numeric form brings words with similar meanings together. For example, converting the words "prince" and "king" into numeric vectors brings them closer together in higher dimensional space because they have similar meanings.
  3. Parsing and Lexical Labeling
    • Label each tokenized word with its lexical gender (e.g., noun, verb, adjective, etc.). For example, for this sentence, "The smart student answered the question." , labeled "student = noun" and "answer = verb."
  4. Word and phrase frequency
    • Count the frequency of each word or phrase in the text. For example, count the frequency of "cat" in the text, if it occurs 5 times, it is recorded as "cat: 5".
  5. N meta-syntax
    • Splits text into fixed-length word sequences (unigrams, bigrams, trigrams, etc.). For example, in the sentence "I love apples." the generated binary grammars (bigrams) are ["I love", "love to eat", "eat apples"].
  6. Noun phrase extraction
    • Identify the noun phrase in a sentence, usually as the subject or object. For example, in the sentence "Beautiful flowers bloomed." extract the noun phrase "beautiful flowers."
  7. emotional analysis
    • Analyze the emotional tendency of a text and assess its degree of positivity or negativity. For example, analyze the sentence "This movie is great!" Derive a positive mood score, e.g., 0.8 (positive).
  8. morphology (linguistics)
    • Get the singular or plural form of a word. This piece is actually better understood in English because they have a specialized way of writing it, if in Chinese, e.g. convert the plural form of the word "dogs" to "dogs".
  9. morphological restoration
    • Find the root or center word of the word. For example, the words "fly," "fly," and "flight" are reduced to the root word "fly."
  10. WordNet
    • A database containing synonyms, antonyms and detailed information is very useful for the construction of language tools. For example, a query for the word "happy" yields synonyms such as "happy" and "pleasant" and antonyms such as "sad" and "happy". ".

Looking at it this way, even a short sentence requires a lot of processing before we can come to a valid conclusion so that the machine can respond like a human. Luckily, however, Python provides a multitude of Natural Language Processing (NLP) libraries and dependency packages at our disposal. The existence of these tools allows us to quickly analyze text and obtain results without having to delve into complex underlying implementations, just by making simple API calls, truly eliminating the need to repeatedly build wheels.

Next, we can take the brainless version of the chatbot we just based on as a starting point and add further functionality for sentiment analysis and noun extraction. Sentiment analysis will enable the bot to recognize the user's emotional state, while noun extraction will help it to catch the key content in the conversation. This will allow us to respond to users' questions more effectively, making communication smoother and more natural.

Sentiment Analysis Edition Chat

We've just covered the TextBlob library, so we won't go into that again here, but if you wish to dive deeper into this powerful natural language processing library, here's also theGetting Started Links, to help you better understand its usage.

Now, we can simply implement the code for a chatbot. The main purpose of this bot is to first analyze the typed sentence and determine if the sentiment is positive or negative. If the user's input mentions certain terms, we will incorporate these terms into the bot's response and actively ask the user about them to create a more natural and emotional communication atmosphere.

code implementation

Next, let's look at the exact code implementation:

import random
from textblob import TextBlob
from textblob.np_extractors import ConllExtractor
import nltk

# downloading NLTK resource (such as manpower or tourism)
("punkt_tab")
('conll2000')

extractor = ConllExtractor()

def main():   
    print("Hello, I am Marvin, the friendly robot.")
    print("You can end this conversation at any time by typing 'bye'")
    print("After typing each answer, press 'enter'")
    print("How are you today?")

    while True:
        # wait for the user to enter some text
        user_input = input("> ")

        if user_input.lower() == "bye":            
            # if they typed in 'bye' (or even BYE, ByE, byE etc.), break out of the loop
            break
        else:
            # Create a TextBlob based on the user input. Then extract the noun phrases
            user_input_blob = TextBlob(user_input, np_extractor=extractor)
            np = user_input_blob.noun_phrases
            response = ""
            if user_input_blob.polarity <= -0.5:
                response = "Oh dear, that sounds bad. "
            elif user_input_blob.polarity <= 0:
                response = "Hmm, that's not great. "
            elif user_input_blob.polarity <= 0.5:
                response = "Well, that sounds positive. "
            elif user_input_blob.polarity <= 1:
                response = "Wow, that sounds great. "

            if len(np) != 0:
                # There was at least one noun phrase detected, so ask about that and pluralise it
                # . cat -> cats or mouse -> mice
                response = response + "Can you tell me more about " + np[0].pluralize() + "?"
            else:
                response = response + "Can you tell me more?"
            print(response)
    
    print("It was nice talking to you, goodbye!")

# Start the program
main()

The functionality of this code can be roughly divided into the following sections:

  1. Initialize the extractor: Create an instance of a noun phrase extractorextractor. This extractor will be used to recognize important noun phrases in user input.
  2. main function：
    • Initiate a dialog with the user with welcome messages and prompts.
    • Enter a loop and wait for user input.
    • If the user types "bye", the program ends the conversation.
    • Otherwise, use theTextBlob Create an object to analyze user input:
      • Extract noun phrases.
      • Generate different responses (from negative to positive) based on the emotional polarity of the text.
      • If noun phrases are detected, ask the user for more information about these noun phrases and change the noun phrases to the plural form.
      • If no noun phrase is detected, the user is asked for more information.
  3. Conclusion of dialogues: When the user enters "bye", the program prints a farewell message and ends.

Through sentiment analysis and noun phrase extraction, the robot is able to provide a more targeted response, which will obviously seem more interactive and responsive than previous brainless robots. Let's see it in action:

image

summarize

In exploring Natural Language Processing (NLP), we learned how to build a basic chatbot that implements a step-by-step optimization process from random responses to sentiment analysis. By using Python and its powerful libraries such as TextBlob, we were able to easily process text data and extract valuable information.

Today, we introduced the basic concepts and common tasks of NLP, covering tokenization, sentiment analysis, noun phrase extraction, and more. These skills not only help us build simple chatbots, but also lay the foundation for delving into more complex NLP problems later.

In the future, we will continue to explore more advanced NLP techniques to further enhance the robot's intelligence and interaction capabilities. Let's look forward to the next learning journey together!

I'm Rain, a Java server-side coder, studying the mysteries of AI technology. I love technical communication and sharing, and I am passionate about open source community. I am also a Tencent Cloud Creative Star, Ali Cloud Expert Blogger, Huawei Cloud Enjoyment Expert, and Nuggets Excellent Author.

💡 I won't be shy about sharing my personal explorations and experiences on the path of technology, in the hope that I can bring some inspiration and help to your learning and growth.

🌟 Welcome to the effortless drizzle! 🌟

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