Ever wonder how GPT understands your prompt? By now many of you have probably got the habit of giving ChatGPT an entire article to summarize and generate a new content. Before we learn how to write better with ChatGPT, let's under the 4 steps the technologies and techniques ChatGPT uses to comprehand your requests.
Step 1: Data Acquisition
All AI requires user input. In ChatGPT, the user input is typically referred to as the "prompt." It's the text or query provided by the user to initiate a conversation or request a specific response from the model. The term "request" is more commonly associated with actions made to external systems or APIs to fetch data or perform certain tasks, whereas "prompt" specifically denotes the input text provided to ChatGPT for generating a response.
Step 2: Text Preprocessing
Text data often contains noise such as HTML tags, special characters, or punctuation that can interfere with analysis. Cleaning the text involves removing these elements to ensure that only the relevant content remains. Additionally, ChatGPT standardizes the text by converting it to lowercase, so that words like "Hello" and "hello" are treated the same. Tokenization breaks the text into smaller units, typically words or sentences, which makes it easier for the computer to process. Stopwords, common words that don't carry much meaning like "the" or "is", are removed to focus on the more informative words. Finally, lemmatization or stemming reduces words to their base or root form, so variations of the same word (e.g., "running", "ran", "runs") are treated as the same.
Step 3: Text Representation
Computers can't understand text directly, so ChatGPT needs to represent it numerically. In this step, ChatGPT applies NLP algorithms to analyze the content of the article and extract keywords based on their frequency, relevance, and importance within the text. It is crucial for content marketers looking to optimize their content for search engines and target specific keywords or topics that are relevant to their audience. Here are simplified explanations of common techniques used to convert text into numerical representations:
Bag of Words (BoW)
Imagine you have a bag and you're putting all the words from a document into it. BoW counts how many times each word appears in the document and creates a list with numbers representing these counts. Each document gets its own list, so you end up with a matrix where each row represents a document and each column represents a word. It's like making a tally chart of words in each document.
TF-IDF (Term Frequency-Inverse Document Frequency)
TF-IDF is similar to BoW, but it also considers how often a word appears across all documents in a collection (corpus). It gives more weight to words that are rare in the corpus but frequent in the document you're analyzing. So, instead of just counting occurrences, TF-IDF adjusts for how important a word is in a specific document compared to its importance across all documents.
Word Embeddings
Word embeddings are like secret codes that represent words in a way that captures their meaning and relationships with other words. Each word is assigned a dense vector (a series of numbers) in a continuous vector space. Similar words end up having vectors that are close together, while words with different meanings are farther apart. It's like placing words on a map where similar words are grouped together based on their context in sentences.
Step 4: NLP Analysis
NLP Analysis involves using techniques from Natural Language Processing (NLP) to extract meaningful insights and information from textual data. It encompasses various tasks such as sentiment analysis, named entity recognition, part-of-speech tagging, text summarization, and topic modeling.
Sentiment Analysis
Sentiment analysis determines the overall mood or sentiment expressed in the text. It categorizes the text as positive, negative, or neutral based on the language used. This helps ChatGPT understand the emotional tone of the content.
Named Entity Recognition (NER)
Named Entity Recognition (NER) identifies and classifies specific named entities mentioned in the text. These entities can include people's names, organizations, locations, dates, and more. NER helps ChatGPT extract important information and understand the context of the text better.
Part-of-Speech (POS) Tagging
Part-of-Speech (POS) tagging assigns grammatical categories to each word in the text. It labels words as nouns, verbs, adjectives, adverbs, and so on. POS tagging helps ChatGPT understand the syntactic structure of the text and how words function within sentences.
Text Summarization
Text summarization condenses the content of the text into a shorter version while retaining the essential information. It captures the main points and key details, making it easier to understand the gist of the text quickly. Text summarization helps ChatGPT process large volumes of text more efficiently.
Topic Modeling
Topic modeling identifies the main themes or topics discussed in the text. It clusters words or phrases that frequently occur together, revealing underlying patterns and subjects. Topic modeling helps ChatGPT grasp the primary focus of the text and understand its content at a higher level.
