Have you ever wondered how chatGPT can answer your questions in seconds? How Siri can understand your voice commands and respond with natural speech? How Bing AI can hat with you like a friend and give you advice and recommendations? These are all examples of natural language processing (NLP), a branch of artificial intelligence that enables computers to understand and communicate with humans using natural language.

NLP is one of the most exciting and rapidly evolving fields of AI, with applications in various domains such as web search, customer service, social media, education, healthcare, and more. But how does NLP work? How can computers process and analyze large amounts of text data and extract meaningful insights from them? In this article, we will explore some of the basic concepts and techniques of NLP for text understanding.

What is text understanding?

Text understanding is the task of comprehending the meaning and intent of a given piece of text. It involves analyzing the structure, content, context, and sentiment of the text, as well as identifying the entities, relations, events, facts, opinions, and emotions expressed in it. Text understanding can also involve answering questions about the text or generating summaries or paraphrases of it.

Text understanding is a challenging problem because human language is complex and ambiguous. There are many ways to express the same idea using different words or sentences. There are also many linguistic phenomena that make text understanding difficult for computers, such as synonyms (words with similar meanings), homonyms (words with multiple meanings), idioms (phrases with figurative meanings), metaphors (comparisons between unrelated things), sarcasm (ironic statements that convey the opposite meaning), and so on.

How does NLP help with text understanding?

NLP uses a combination of computational linguistics (rule-based modeling of human language) and machine learning (statistical or neural modeling of language data) to help computers process and understand natural language. NLP typically involves several subtasks that break down human text into smaller units that can be analyzed more easily by computers. Some of these subtasks include:

- Speech recognition: converting voice data into text data

- Tokenization: splitting text into words or symbols

- Part-of-speech tagging: assigning grammatical categories to words

- Lemmatization: reducing words to their base forms

- Stemming: removing word endings

- Named entity recognition: identifying proper nouns or categories in text

- Dependency parsing: analyzing the syntactic relationships between words

- Semantic parsing: analyzing the logical structure and meaning of sentences

- Coreference resolution: linking pronouns or other references to their antecedents

- Sentiment analysis: determining the attitude or emotion of the speaker or writer

- Topic modeling: discovering the main themes or topics in a document or corpus

- Information extraction: extracting structured information from unstructured text

- Information retrieval: finding relevant documents or passages for a query

- Question answering: answering natural language questions based on a document or corpus

- Text summarization: generating concise summaries of long texts

- Text generation: producing natural language texts from scratch

These subtasks can be performed using different methods depending on the type and amount of data available,

the complexity and specificity of the task,

and the desired level of accuracy and efficiency.

Some common methods include:

-Rule-based methods:

using predefined rules or patterns to analyze or generate texts based on linguistic knowledge

-Statistical methods:

using probabilistic models to learn from large amount of labeled or unlabeled data

-Neural network methods:

using deep learning models to learn complex representations and patterns from data

Let's look at some examples of how these methods can be used for text understanding.

Example 1: Text summarization

Text summarization is the task of generating a short and concise summary of a long text. It can be useful for extracting the main points or highlights of a document, such as a news article, a research paper, or a book review.

One way to perform text summarization is to use statistical methods based on frequency and importance scores. For example, we can use the TF-IDF (term frequency-inverse document frequency) measure to assign weights to words based on how often they appear in the document and how rare they are in the corpus. Then we can select the sentences that contain the most weighted words and combine them into a summary.

Another way to perform text summarization is to use neural network methods based on sequence-to-sequence models. These models consist of two parts: an encoder that encodes the input text into a vector representation, and a decoder that generates the output summary from the vector representation. The encoder and decoder are trained jointly using large amounts of text-summary pairs.

Here is a sample code for text summarization using Python:

# Importing libraries
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize

# Sample text to be summarized
text = "TEXT TO BE SUMMARIZED"
# Defining stopwords
stopWords = set(stopwords.words("english"))

# Tokenizing words
words = word_tokenize(text)

# Building frequency table for words
freqTable = dict()
for word in words:
    word = word.lower()
    if word in stopWords:
        continue
    if word not in freqTable:
        freqTable[word] = 1
    else:
        freqTable[word] += 1
# Tokenizing sentences
sentences = sent_tokenize(text)

# Building sentence scores based on word frequencies
sentenceScores = dict()
for sentence in sentences:
    for word in word_tokenize(sentence.lower()):
        if word in freqTable:
            if sentence not in sentenceScores:
                sentenceScores[sentence] = freqTable[word]
            else:
                sentenceScores[sentence] += freqTable[word]

# Selecting the top N sentences with highest scores
import heapq
summary_sentences = heapq.nlargest(7, sentenceScores, key=sentenceScores.get)

# Joining the sentences together
summary = ' '.join(summary_sentences)
print(summary)

Note that this is just a sample code just to give you an idea and would need a lot of tweaking to actually work for your use case

Example 2: Question answering

Question answering is the task of answering natural language questions based on a given document or corpus. It can be useful for providing information or knowledge to users in an interactive way.

One way to perform question answering is to use rule-based methods based on syntactic and semantic analysis. For example, we can use dependency parsing to identify the subject, predicate, and object of a question, and semantic parsing to map the question to a logical form. Then we can use information retrieval techniques to find relevant documents or passages that match the logical form, and extract or generate answers from them.

Another way to perform question answering is to use neural network methods based on attention mechanisms. These mechanisms allow the model to focus on different parts of the input and output sequences depending on their relevance. For example, we can use an attention-based sequence-to-sequence model that encodes both the question and the document into a vector representation, and decodes the answer from the vector representation while attending to relevant parts of the question and document.

# Importing libraries
import nltk
import numpy as np
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

text = "TEXT TO BE USED AS CONTEXT"
# Defining stopwords
stopWords = set(stopwords.words("english"))

# Tokenizing sentences
sentences = sent_tokenize(text)

# Building TF-IDF matrix for sentences
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(sentences)

# Asking user for question input
question = input("Please enter your question: ")

# Tokenizing question
question = word_tokenize(question)

# Removing stopwords from question
question = [word for word in question if word not in stopWords]

# Building query vector for question
query_vector = vectorizer.transform([' '.join(question)]).toarray()

# Finding cosine similarity between question and sentences
cosine_similarities = cosine_similarity(query_vector, X).flatten()

# Finding the most similar sentence to the question
most_similar_index = np.argmax(cosine_similarities)

# Printing the answer
answer = sentences[most_similar_index]
print(answer)

This is also just a sample code

Bottom Line

In this article, we have introduced some of the basic concepts and techniques of NLP for text understanding. We have seen how NLP can help computers process and analyze large amounts of text data and extract meaningful insights from them. We have also seen some examples of how NLP can be applied to various tasks such as text summarization and question answering.

NLP is a fascinating and rapidly evolving field of AI, with many challenges and opportunities ahead. As more and more data becomes available in natural language form, NLP will play a crucial role in making sense of it and enabling humans to interact with it in natural ways.