Is it possible to develop Natural Language Processing solutions without leveraging text annotation services? Can the machines learn on their own the nuances of human language and perform their tasks efficiently? To answer these questions, one needs to have a complete understanding of text annotation and NLP and the relation between them.
What is Text Annotation?
Text Annotation involves the identification and extraction of insights from text or audio recordings. It is a process of detecting and tagging the meaningful words from the text. It helps to add vital information to raw data and makes it comprehensible for machines to carry out tasks.
Types of Text Annotation Services
Entity annotation is the process of detecting, extracting, and annotating entities within the text. It involves locating and tagging a particular part of the text that contains proper names or functional elements of speech such as nouns, verbs, adverbs, and adjectives.
This type of text annotation involves identifying the relationship between two entities and capturing how they are related. Entity linking is carried out in two ways; linking two entities within a text and linking the entities to knowledge databases related to them.
Document classification also known as text classification involves classification of an entire document or a line of text with a single label. Text annotators analyze the content and identify the intent, subject & sentiment within the content and assign a predetermined category related to it.
It involves detecting the emotion hidden within a text or email. Sentiment annotated data helps the machines to understand the sentiment behind the text and helps them to understand the casual forms of communication such as wit, sarcasm, etc.
Linguistic annotation is mainly used for creating labeled datasets for developing Natural Language Processing (NLP) models such as chatbots, translators, virtual assistants among others. It involves annotation of language data in audio recordings and text that mainly comprise phonetic, grammatical, and semantic elements.
NLP is a branch of Artificial Intelligence that enables machines to understand human language. It facilitates the interaction between humans and computers by deriving meaning from human language.
Some of the common examples of NLP include:
- Smart assistants like Siri, Alexa, Cortana, OK Google that detects the patterns in speech.
- Gmail’s email classification categorizes inbox emails into primary, social, or promotions.
- Features like autocomplete, autocorrect that finishes a word or suggests a relevant one and changes words to give meaning to the overall message respectively.
- Language translators that can translate from one language to another
- Search engines like Google that present relevant results based on user intent
Text Annotation Services for NLP
Human language is complex and dynamic and conveys a lot of information. It helps us to understand and also predict human behavior. Computers cannot detect the passing of information through natural languages and for them to do so requires the machines to understand the words as well as the concepts that are connected to give out the intended info.
Since natural languages are not native to machines, they need to be taught using intermediate data structures (labeled data) and help them understand what people want from texts. Text labeling services convert unstructured data into a structured one which is then used to train the NLP algorithms to extract meaning associated with sentences and collect useful data from them.
Hence, one can say high-quality annotated data is a fundamental part of the NLP ecosystem. Without text annotation services, it is may not be possible to build an effectively functioning NLP algorithm.