- "Document classification or document categorization is a problem in library science, information science, and computer science."
Dividing the text into different categories based on the content.
Natural Language Processing (NLP): The field of computational linguistics that focuses on the interactions between computers and human language.
Feature Extraction: The process of converting raw text data into numerical features that can be used as input to machine learning models.
Text Preprocessing: The process of cleaning and transforming text data to make it suitable for machine learning tasks, such as removing stop words, stemming, and lemmatization.
Corpus: A collection of texts used for natural language processing research or training machine learning models.
Tokenization: The process of dividing a text into smaller units or tokens, which are usually words or sentences.
Bag-of-Words Model: A textual representation of a document that disregards the order and structure of the text, and only counts the occurrence of words.
Term Frequency-Inverse Document Frequency (TF-IDF): A numerical measure that reflects how important a word is to a document in a corpus.
Machine Learning Algorithms: A class of algorithms that use statistical models to find patterns in data and make predictions or classifications.
Naive Bayes Classifier: A probabilistic classifier that assigns a probability to each class and chooses the one with the highest probability.
Support Vector Machine (SVM): A binary classification algorithm that finds the hyperplane that best separates data points into different classes.
Decision Trees: A tree-based model that maps observations about an item to conclusions about its target value.
Neural Networks: A class of models that simulate the functions of the human brain and can be used to solve a wide range of machine learning problems.
Transfer Learning: The practice of using pre-trained models to improve the performance of new models in related tasks.
Evaluation Metrics: Measures used to assess the performance of a text classification model, such as accuracy, precision, recall, and F1 score.
Sentiment Analysis: This type of text classification aims to identify the emotions or opinions conveyed in a piece of text, such as positive or negative sentiment.
Topic Classification: This type of text classification focuses on the topics discussed in a piece of text and categorizes it into pre-defined topics.
Intent Classification: This form of text classification helps in identifying the intent behind the user's input, such as whether they intend to buy a product or ask a question.
Named Entity Recognition: This form of text classification identifies different entities present in the text, such as people, organizations, locations, or dates.
Language Identification: This type of text classification helps in identifying the language of the text.
Text Categorization: This form of text classification categorizes documents or text into pre-defined categories, such as news article or blog post.
Spam Filtering: This form of text classification filters out unwanted messages or emails by identifying spam content.
Authorship Attribution: This type of text classification helps in identifying the author of a particular piece of text by analyzing their writing style.
Document Summarization: This form of text classification aims to summarize a large piece of text into a shorter version while preserving the essential information.
Question Answering: This type of text classification helps in answering a user's question by analyzing the question and providing the most relevant answer.
Text Clustering: This form of text classification groups together similar pieces of text based on the similarity of their content.
Document Classification: This type of text classification groups together similar documents based on their contents.
Mood Classification: This form of text classification helps in identifying the mood or emotional state conveyed in a piece of text.
Opinion Mining: This type of text classification identifies the opinions expressed in a piece of text, such as whether they are positive, negative, or neutral.
Information Extraction: This form of text classification extracts specific pieces of information from a larger text, such as phone numbers or email addresses.
- "The task is to assign a document to one or more classes or categories. This may be done 'manually' (or 'intellectually') or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science."
- "The documents to be classified may be texts, images, music, etc."
- "Each kind of document possesses its special classification problems."
- "In the rest of this article only subject classification is considered."
- "Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year, etc.)."
- "There are two main philosophies of subject classification of documents: the content-based approach and the request-based approach."
- "The intellectual classification of documents has mostly been the province of library science."
- "The algorithmic classification of documents is mainly in information science and computer science."
- "The problems are overlapping, however, and there is, therefore, interdisciplinary research on document classification."
- The overall goal of document classification is to "assign a document to one or more classes or categories."
- "This may be done 'manually' (or 'intellectually') or algorithmically."
- "Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year, etc.)."
- "In the rest of this article only subject classification is considered."
- "The documents to be classified may be texts, images, music, etc."
- "The intellectual classification of documents has mostly been the province of library science."
- "The algorithmic classification of documents is mainly in information science and computer science."
- "There are two main philosophies of subject classification of documents: the content-based approach and the request-based approach."
- "Each kind of document possesses its special classification problems."
- "The problems are overlapping, however, and there is, therefore, interdisciplinary research on document classification."