Natural Language Processing Research Areas
Introduction
Natural Language Processing is a branch of artificial intelligence and linguistics that deals with the interactions between computers and the human language. Its objective is to read and understand the spoken languages. It is a field of study that has gained prominence due to the incredible potential it has in simplifying human computer interactions. In this blog, I will present some of the most researched areas in Natural Language Processing.
Machine Translation
Machine Translation is used to automatically translate text through a computer, without any human involvement. This research area is incredibly successful as tools like Google Translate can instantly translate words and text sequences (phrases, sentences, and paragraphs) between English and over 100 other languages. It remains a highly researched topic due to the challenges that arise from the inherent ambiguity, flexibility, and constant evolution of human language.
Question Answering
Question Answering is concerned with the task of automatically answering questions posed by humans in natural language. They are mainly used in applications for customer service and virtual assistants. The main challenge of this research area is understanding the context and polarity of a given conversation. These systems may also require constant maintenance as the user’s questions and commands can change over time.
Language Modelling
Language modeling is the task of predicting the next word or character in a document. Many of us interact with systems that include language modelling when we write a text message or an email. The importance of this research topic resides in its ability to help machines understand incomplete text and how it can be used to aid question answering systems and machine translation applications.
Text Classification
Text Classification automatically categorizes text into groups. Probably the most common application that uses text classification is email spam detection. Having said that other common examples of text classification systems include:
- Sentiment Analysis: The process that determines the polarity of a given text. In other words, the process that identifies if the comments related about a certain topic is positive or negative. These type of classifiers are used to understand user reviews about a specific service or product.
- Topic Detection: The process of understanding the theme of a given text. This is used for document classification systems that can automatically route documents through the various departments of a given company or organization.
- Language Detection: The process of detecting the language of a given text. Similarly to the document classification systems, language detection can be used to automatically route support tickets written in English or Spanish to the corresponding teams.
Text Generation
Text Generation is used to automatically generate natural language text. It can be used to generate the first draft of financial reports and reduce working hours that could have been spent on more valuable tasks. These types of systems are incredibly controversial as they can be used to create fake news or be used for propaganda and disinformation.
Text Summarization
Text Summarization is the process of shortening long pieces of text by extracting the most important points outlined in a document. The importance of text summarization resides in the fact that it can be used to reduce reading time and accelerate research tasks.
Named Entity Recognition
Named Entity Recognition (NER) is a type of text classification task that categorizes entities in text. The most common entity categories are:
- Person (e.g. Michael Jordan, Elon Musk, Isaac Newton)
- Organization (e.g. Duke University, Facebook, Marvel Studios)
- Time (e.g. 2020, November 12th, 2:00pm)
- Location (e.g. Staples Center, New York, Central Park)
NER can be useful for search engines by improving the relevance of the search results.
Relation Extraction
Relation Extraction is the task of extracting semantic relationships from text. The relationships mostly occur between two or more entities. Relationships can be represented in any number of semantic categories (e.g. lives in, works with, married to, etc…). For example, the relation extraction system can be used to identify in a given text that Person A is married to Person B.
Semantic Parsing
Semantic parsing is the task of translating natural language into a formal meaning representation on which a machine can act. These types of applications are extremely useful in information retrieval and question answering systems.
Word Sense Disambiguation
Word sense disambiguation is used to determine which meaning of word is activated by the use of word in a particular context. Words can have different meaning based on the context that it is used in. This type of natural language task has the ability to reduce ambiguity in text and, therefore, limit the possible misunderstandings between humans and computers for applications involving machine translation and question answering.
Grammatical Error Correction
Grammatical Error Correction is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. Writing assistants like Grammarly helps people write correctly while composing any type of document, emails, and social media posts.
Intent Detection
Intent Detection is used to automatically associate any given text to a specific purpose or goal. It is a classifier that analyzes pieces of text and categorizes them into intents to carry a specific action or actions. It is an essential part of any natural language understanding system used in virtual assistants.
Final Remarks
The presented areas of research in Natural Language Processing in this blog are just a few examples of what is being done in the field. NLP software is still far from perfect but it has come a long way in recent years. They have become better than humans at some tasks like language translation, but they are still limited when it comes to complex communication.
