NLP Basics: Core Concepts, Task Types, and Preprocessing Steps
The article introduces Natural Language Processing as an AI subfield, outlines its four main task categories—classification to sequence, sequence to classification, synchronous and asynchronous seq‑to‑seq—and details the typical preprocessing pipeline including corpus collection, cleaning, tokenization, stemming, lemmatization, POS tagging, NER, and chunking.
Natural Language Processing (NLP) is an AI subfield that studies how computers can understand, process, and generate human language.
The author classifies NLP tasks into four categories: (1) classification‑to‑sequence, (2) sequence‑to‑classification, (3) synchronous sequence‑to‑sequence, and (4) asynchronous sequence‑to‑sequence. In this view, “class” refers to a label or category, while “sequence” denotes a text or array; NLP essentially transforms one data type into another, similar to most machine‑learning models.
To perform these tasks, a typical preprocessing pipeline is required. It starts with corpus collection, followed by text cleaning, tokenization, optional stop‑word removal, normalization, and feature extraction.
The standard six steps for English NLP preprocessing are:
Tokenization
Stemming
Lemmatization
Parts of Speech tagging
Named Entity Recognition (NER)
Chunking
Signed-in readers can open the original source through BestHub's protected redirect.
This article has been distilled and summarized from source material, then republished for learning and reference. If you believe it infringes your rights, please contactand we will review it promptly.
Lisa Notes
Lisa's notes: musings on daily life, work, study, personal growth, and casual reflections.
How this landed with the community
Was this worth your time?
0 Comments
Thoughtful readers leave field notes, pushback, and hard-won operational detail here.