Outsource text annotation services to Infosearch, a leading annotation services company. Text annotation belongs to the foundational steps of the AI and machine learning field, especially when processing textual data, whereas NLP helps in deriving meaning from the data. Here’s an overview of their differences:
Text Annotation:
Definition:
Text annotation is the activity of giving a text some form
of a tag that enhances its usability in ML algorithms.
Purpose:
The primary objective is to obtain datasets with measures to
categorize data which will in turn be used in training as well as testing
Machine learning algorithms. It helps in analyzing the text to detect various
characteristics of the text like Subheads, Keywords, Looped words, Positive and
Negative words, etc.
Types of Annotation:
1. Entity Annotation:
Writing over entities like names, dates or place names.
2. Part-of-Speech
Tagging: Defining each word in a sentence as a noun a verb or an adjective
etc.
3. Sentiment
Annotation: Assigning the textual polarity where the text may be positive,
negative or a combination of both or a neutral text.
4. Intent Annotation:
Determine the purpose a message plays, which is a concept commonly employed in
Smart bots.
5. Linguistic
Annotation: Differentiating between different aspects of the text by
categorizing the words or putting grammatical/syntactic labels on the
material.
Tools:
Currently, there are different approaches for text
annotation, some of them are Labelbox, Prodigy, Brat, etc.
Manual vs. Automated:
- Manual Annotation: Performed manually, accurate but it
requires a lot of time, and money and sometimes it is unavailable.
- Automated Annotation: This can be done using AI tools and is
fast but the results may need to be checked by a human being.
Natural Language Processing (NLP)
Definition:
NLP is the sub-discipline of AI and entails the use of
natural language by computers and human beings. It can be defined as the
process of analyzing, comprehending and producing the natural language of people.
Purpose:
The objective of NLP is to get the machines or systems to
understand, analyze, and comprehend human language content and produce useful
outputs concerning the content.
Applications:
1. Machine Translation: Converting word or passage from one
language to another language.
2. Sentiment Analysis: Establishing the opinion conveyed in
a text.
3. Text Summarization: Summarizing a larger body of texts
where the goal is to present the subject in the shortest possible number of
words.
4. Chatbots: To empower the conversational agents that can
be run autonomously.
5. Speech Recognition: Transcribing of spoken language into
written language.
6. Text Generation: Generating proper context-sensitive text
and grammatically coherent text.
Techniques:
1. Tokenization: The process of segmenting text content into
word-by-word or phrase-by-phrase units of meaning.
2. Parsing: Looking at the grammatical construction of a
sentence.
3. Stemming and Lemmatization: Forming by cutting down to
the main stem of a word, or word element.
4. Named Entity Recognition (NER): Tagging of entities in
text: automatic detection and categorization.
5. Sentiment Analysis: Supposing the sentiment of a text.
Tools and Frameworks:
Some commonly used NLP libraries/toolkits are NLTK, SpaCy,
BERT, GPT, and Stanford NLP.
Key Differences
- Scope: Text annotation is widely used to develop datasets
for training NLP models and it is the preliminary action taken. NLP includes a
wider spectrum of tasks that are associated with text and speech processing.
- Process vs. Application: Generally, text annotation is
about putting information tags into a text while on the other hand, NLP is
about creating models that can interpret human language and create or complete
language tasks.
- Role in AI: Text annotation is commonly a manual or a
semiautomatic process involved in the pre-processing of data before feeding it to
the machine learning algorithms. NLP uses these annotated data to train
artificial systems which can accomplish related tasks with the natural
language.
In conclusion, text annotation and NLP are related but are
used in different contexts in the integration of the AI environment. Text
annotation offers the basic annotated data that NLP requires for the creation
and fine-tuning of the models; hence, the term is as fundamental to NLP as the
models that come with it are crucial to the enhancement of different language
processing tasks.
