Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics that deals with the interactions between computers and human (natural) languages. It focuses on the development of algorithms and models that can understand and interpret natural language, and that can be used to solve a wide range of language-related tasks and problems.
Some of the main tasks of natural language processing include:
- Text classification and categorization: This involves automatically assigning labels or categories to text documents or sentences based on their content and meaning. For example, a text classification system might be used to classify a news article as belonging to a specific topic or category, such as politics, sports, or entertainment.
- Sentiment analysis: This involves automatically detecting and analyzing the emotional content of text, in order to determine the overall sentiment or attitude of the writer. For example, a sentiment analysis system might be used to analyze the sentiment of customer reviews, in order to determine the overall satisfaction or dissatisfaction of the customers.
- Named entity recognition: This involves automatically identifying and classifying named entities, such as people, organizations, locations, and dates, in text. For example, a named entity recognition system might be used to identify the names of people, places, and organizations in a news article, in order to extract important information and facts.
- Part-of-speech tagging: This involves automatically identifying and labeling the parts of speech, such as nouns, verbs, adjectives, and adverbs, in a sentence. For example, a part-of-speech tagging system might be used to identify the different parts of speech in a sentence, in order to analyze its structure and meaning.
- Syntactic parsing: This involves automatically analyzing the grammatical structure of a sentence, and representing it in a parse tree or other formal representation. For example, a syntactic parser might be used to analyze the structure of a sentence, in order to identify its subject, verb, and object, and to determine the relationships between different words and phrases in the sentence.
- nlpprogress.com .. tracking progress in natural language processing
- Category:Tasks of natural language processing
- Natural-language processing: Major evaluations and tasks
Anaphora | Automatic Summarization | Collocation Extraction | Language Identification | Lemmatization | Linguistic Empathy | Machine Translation | Named-entity Recognition | Part-of-speech Tagging | Phrase Chunking | Relationship Extraction | Semantic Role Labeling | Shallow Parsing | Stemming | Terminology Extraction | Text Segmentation | Textual Entailment | Tokenization | Truecasing
Tasks Of Natural Language Processing
|Major Evaluations And Tasks|
|1) Anaphora (Anaphora Resolver) .. a type of expression whose reference depends on another referential element ..
3) Collocation Extraction (Collocation Extractor) .. the task of extracting collocations automatically from a corpus ..
6) Linguistic Empathy .. the “point of view” in an anaphoric utterance ..
14) Sentence Boundary Disambiguation (Sentence Boundary Disambiguator/Disambiguators) .. the problem of deciding where sentences begin and end ..
18) Text Segmentation (Text Segmenter/Segmenters) .. dividing written text into meaningful units ..
19) Textual Entailment (Textual Entailer/Entailers) .. a directional relation between text fragments ..
21) Truecasing (True-casing/Truecaser) .. determining the proper capitalization of words ..
22) Word-sense Disambiguation (Word-sense Disambiguator/Disambiguators) .. the process of identifying which sense of a word is used, when the word has multiple meanings ..
|1) Automatic summarization
2) Coreference resolution
3) Discourse analysis
5) Lexical semantics
6) Machine translation
7) Morphological segmentation
8) Named entity recognition
10) Natural language understanding
11) Optical character recognition
13) Part-of-speech tagging
15) Recognizing Textual entailment
16) Relationship extraction
17) Sentence breaking
18) Sentiment analysis
19) Speech recognition (Speech-to-text)
20) Speech segmentation
22) Terminology extraction
23) Text-to-speech (Speech synthesis)
24) Topic segmentation
25) Word segmentation
26) Word sense disambiguation