NLP technologies
NLP, a branch of artificial intelligence (AI), enables computers to understand natural human language – the words and sentences we use – and create value from it.
NLP technologies are used as part of intelligent document processing (IDP) to extract actionable data and insights from unstructured text and semi-structured data, and info streams like social media captions.
Natural Language Processing services utilize a number of techniques, all of which are offered at NLP consulting companies like Netguru.
This automated data processing technique retrieves specific info relating to a selected topic from one or more bodies of text. Using IE, you can extract information from unstructured and semi-structured data, and structured data that contains machine-readable text.
Information extractions spans automatic annotation, content recognition, and data extraction from images and video. For the purposes of natural language processing, IE is primarily used to extract structured data from unstructured data.
Text generation and summarization
This condenses info in a large body of text into a smaller or shorter form that’s quicker to read or consume. Extractive text summarization identifies the most important sentences and joins them to create a summary. Abstractive summarization picks out the most important parts, interprets their context, and reproduces them in a new way.
Named entity recognition
When thinking about document processing analysis, entities are the main components of a sentence, and they include nouns and verbs. Broadening that, named entity recognition is an NLP technique that automatically scans a body of text, picks out fundamental entities, and then places them into predefined categories.
It processes large volumes of text and identifies entities like names, dates, times, locations, companies, and monetary values, helping you organize data more efficiently.
This natural language processing technique is also known as entity identification, entity extraction, or entity chunking. Uses range from instantly extracting relevant information about candidates during a recruitment process to classifying content for news channels.
Text classification
Aka texting tagging or text categorization, this uses natural language processing to automatically analyze text and assign it a set of predefined tags or categories based on the content.
Text classification is an efficient and effective alternative to manual data entry and processing, one of the foundations for sentiment analysis, and plays a role in topic detection and language detection.
Text similarity
This NLP technique establishes how close two pieces of text are in both word construction (lexical similarity) and meaning (semantic similarity).
Question answering
An important NLP technique, question answering allows you to ask a question to context text, and then your ML model finds the most appropriate answer from that context text – if it exists.
This is an extension of named entity recognition. It extracts semantic relationships from a body of text, typically between two or more entities of a certain type such as a person, organization, or location.
These entities fall into semantic categories such as “married to”, “employed by”, or “lives in”. Relationship extraction is used when listing found entities isn’t enough, and you need to know the relationship between them.
Sentiment analysis
Also known as opinion mining, this NLP technique scans relevant data to establish whether the overall view is (very) positive, (very) negative, or neutral.
For example, entity sentiment analysis is used to monitor consumer opinions of products and services, satisfaction levels regarding a company’s customer support, and how well a brand is perceived on social media platforms.
Additional nuances include feelings and emotions, and levels of intent and urgency. Sentiment analysis relies on sophisticated machine learning algorithms and intelligent automation.
Machine translation (MT)
This technique automatically converts one natural language into another. By doing that, you preserve the meaning of the input text while producing fluent text in the output language.
Data labeling or tagging
Essential to making your data preparation worthwhile, this assigns info labels or tags to each raw data sample such as an image and text. The labels are allocated according to the content and context in question and they’re used to advise machine learning models. Three of the most popular data labeling types for image, text, and audio are:
- Natural language processing (NLP) tools like entity annotation and linking, text classification, and phonetic annotation
- Computer vision techniques like image classification, image segmentation, object detection and pose estimation that help machines understand visual data
- Audio processing to identify and tag background noise and develop a transcript of the recorded speech (using NLP algorithms)
Semantic search
Also known as context or semantic analysis, this is a search performed via NLP models that evaluates context and text structure to accurately decipher the meaning of words that have more than one definition.
At NLP companies like Netguru, we use many NLP tools, ranging from a host of machine learning libraries to a selection of pre-trained models. The most popular and fastest-growing programming language is Python. Why? It’s uber-versatile, meaning we can use it for NLP, data science, machine learning, and many more.
In terms of NLP libraries, we use spaCy, NLTK (Natural Language Toolkit), Hugging Face transformers, Gensim, and Spark NLP.
And what about transformers and language models? We rate BERT, RoBERTa, XLNet, and ELECTRA. When it comes to machine learning libraries, we use Pyro, XGBoost, LightGBM, Scikit-Learn, and in terms of deep learning libraries, we’re all about Keras, TensorFlow, and PyTorch.
