NLP Building Blocks
- (Harvard University - Harvard Taiwan Student Association)
- Overview
The process of NLP can be divided into following different stages. Each stage plays a vital role in the overall understanding and processing of natural language.
The Key stages about NLP building blocks:
- Text Preprocessing: The initial step, where raw text is cleaned and transformed into a structured format by processes like tokenization (splitting text into individual words), removing punctuation, and converting text to lowercase.
- Lexical Analysis: Identifying individual words and their meanings within a sentence, often including stemming (reducing words to their root form) and lemmatization (finding the base form of a word).
- Part-of-Speech (POS) Tagging: Assigning grammatical categories to words (like noun, verb, adjective) to understand their role in a sentence.
- Named Entity Recognition (NER): Identifying and classifying specific entities like people, locations, organizations, and dates within a text.
- Syntactic Analysis: Analyzing the grammatical structure of a sentence, including parsing to identify the relationships between words and phrases.
- Semantic Analysis: Understanding the meaning of words and phrases within the context of a sentence, often using techniques like word embedding to represent words as vectors.
- Sentiment Analysis: Determining the emotional tone or sentiment expressed in a piece of text, whether positive, negative, or neutral.
- Discourse analysis: Analyzing how sentences relate to each other within a larger piece of text, considering context and overall meaning.
- Pragmatic analysis: Interpreting the intended meaning of a statement based on the speaker's intent and the situation.
- Text Becomes Insight with Vectorization
In Natural Language Processing (NLP), we convert text into vectors to enable machine learning (ML) models to understand and process textual data by representing words and sentences as numerical values, allowing them to perform calculations and identify patterns within the text, which is essential for tasks like sentiment analysis, text classification, and machine translation, as computers can only work with numbers, not raw text directly.
This process captures the semantic meaning of words and their relationships within a context, leading to more accurate and robust NLP applications.
Key features about text vectorization:
- Machine Learning Compatibility: ML models can only process numerical data, so converting text into vectors allows them to learn patterns and make predictions based on textual information.
- Semantic Understanding: Vectorization techniques like word embeddings capture the semantic relationships between words, meaning words with similar meanings will have similar vector representations.
- Dimensionality Reduction: By converting text into fixed-size vectors, we can manage large amounts of data and perform computations efficiently.
- Contextual Awareness: Advanced vectorization methods can incorporate contextual information, allowing models to better understand the nuances of language depending on the surrounding words.
Examples of text vectorization techniques:
- Bag-of-Words (BoW): Counts the frequency of each word in a document, ignoring word order.
- TF-IDF (Term Frequency-Inverse Document Frequency): Weights words based on their frequency within a document and their rarity across the corpus
- Word Embeddings (like Word2Vec): Represents words as vectors in a high-dimensional space, where similar words have similar vectors
