Editing Natural language processing (section)

=== Lexical semantics (of individual words in context) ===
; [[Lexical semantics]]: What is the computational meaning of individual words in context?
; [[Distributional semantics]]: How can we learn semantic representations from data?
; [[Named entity recognition]] (NER): Given a stream of text, determine which items in the text map to proper names, such as people or places, and what the type of each such name is (e.g. person, location, organization). Although [[capitalization]] can aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of [[named entity]], and in any case, is often inaccurate or insufficient.  For example, the first letter of a sentence is also capitalized, and named entities often span several words, only some of which are capitalized.  Furthermore, many other languages in non-Western scripts (e.g. [[Chinese language|Chinese]] or [[Arabic language|Arabic]]) do not have any capitalization at all, and even languages with capitalization may not consistently use it to distinguish names. For example, [[German language|German]] capitalizes all [[noun]]s, regardless of whether they are names, and [[French language|French]] and [[Spanish language|Spanish]] do not capitalize names that serve as [[adjective]]s. Another name for this task is token classification.<ref>{{Cite journal |last1=Kariampuzha |first1=William |last2=Alyea |first2=Gioconda |last3=Qu |first3=Sue |last4=Sanjak |first4= Jaleal |last5=Mathé |first5=Ewy |last6=Sid |first6=Eric |last7= Chatelaine |first7=Haley |last8=Yadaw |first8=Arjun |last9=Xu |first9=Yanji |last10=Zhu |first10=Qian |date=2023 |title=Precision information extraction for rare disease epidemiology at scale |journal=Journal of Translational Medicine |language=en |volume=21 |issue=1 |page=157 |doi=10.1186/s12967-023-04011-y |pmid=36855134 |pmc=9972634 |doi-access=free }}</ref>

; [[Sentiment analysis]] (see also [[Multimodal sentiment analysis]])
: Sentiment analysis is a computational method used to identify and classify the emotional intent behind text. This technique involves analyzing text to determine whether the expressed sentiment is positive, negative, or neutral. Models for sentiment classification typically utilize inputs such as [[Word n-gram language model|word n-grams]], [[Term frequency-inverse document frequency|Term Frequency-Inverse Document Frequency]] (TF-IDF) features, hand-generated features, or employ [[deep learning]] models designed to recognize both long-term and short-term dependencies in text sequences. The applications of sentiment analysis are diverse, extending to tasks such as categorizing customer reviews on various online platforms.<ref name=":0">{{Cite web |date=2023-01-11 |title=Natural Language Processing (NLP) - A Complete Guide |url=https://www.deeplearning.ai/resources/natural-language-processing/ |access-date=2024-05-05 |website=www.deeplearning.ai |language=en}}</ref>
; [[Terminology extraction]]
:The goal of terminology extraction is to automatically extract relevant terms from a given corpus.
; [[Word-sense disambiguation]] (WSD): Many words have more than one [[Meaning (linguistics)|meaning]]; we have to select the meaning which makes the most sense in context.  For this problem, we are typically given a list of words and associated word senses, e.g. from a dictionary or an online resource such as [[WordNet]].
; [[Entity linking]]: Many words—typically proper names—refer to [[Named entity|named entities]]; here we have to select the entity (a famous individual, a location, a company, etc.) which is referred to in context.