Artificial Intelligence, defined as intelligence exhibited by machines, has many applications in today’s society. One of its applications, most widely used is natural language generation.
What is Natural Language Generation (NLG)?
Natural Language Generation (NLG) simply means producing text from computer data. It acts as a translator and converts the computerized data into natural language representation. In this, a conclusion or text is generated based on collected data and input provided by the user. It is the natural language processing task of generating natural language from a machine representation system. Natural Language Generation in a way acts contrary to Natural language understanding. In natural language understanding the system needs to disambiguate the input sentence to produce the machine representation language, whereas in Natural Language Generation the system needs to make decisions about how to put a concept into words.
The process of generating text can be as simple as keeping a list of readymade text that is copied and pasted. Consequences can either be satisfactory in simple applications such as horoscope machines or generators of personalized business letters. However, a sophisticated NLG system is required to include stages of planning and merging of information to generate text that looks natural and does not become repetitive.
An example of a simple NLG system is the Pollen Forecast for Scotland system which could essentially be a template. NLG system takes as input six numbers, which predict the pollen levels in different parts of Scotland. From these numbers, a short textual summary of pollen levels is generated by the system as its output.
For example, using the historical data for 1 July 2005, the software produces Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country. However, pollen levels will be moderate with values of 4, in Northern areas. In contrast, the actual forecast, which was written by a human meteorologist, from this data was Pollen counts are expected to remain high at level 6 over most of Scotland, and even level 7 in the south-east. The only relief is in the Northern Isles and far northeast of mainland Scotland with medium levels of pollen count.
How does NLG work?
Natural Language Generation (NLG) is a branch of AI that focuses on the automatic generation of human-like language from data. NLG systems take structured data as input and convert it into coherent, contextually relevant human-readable text. The goal is for the generated text to sound like it was written by a human.
Here’s a high-level overview of how Natural Language Generation works:
Data Input: Structured data is the first input used by NLG systems. This information may originate from a number of sources, including spreadsheets, databases, and other organised formats.
Content Planning: Based on an analysis of the input data, the system decides what details to include in the text that is generated. Making choices regarding the selection of content, arrangement, and general structure is required.
Text Planning: The NLG system arranges the content’s natural language expression after it has been decided upon. It chooses the right wording, tone, and style for the text that is generated.
Sentence Generation: Using the planned content as a guide, the system generates individual sentences. Choosing the right words, phrases, and syntactic structures is necessary for this. While some NLG systems generate text using pre-defined templates, others might use more advanced techniques like machine learning.
Coherence and Consistency: Text produced by NLG systems should be consistent and coherent. This entails making certain that the sentences that are produced follow grammatical and stylistic conventions and flow naturally. It might also entail continuing to produce content that is consistent with earlier works.
Refinement: To raise the calibre of the produced text, a refinement procedure may be used. This could entail doing extra proofreading for naturalness, clarity, and grammar.
Stages of Natural Language Generation
- Content determination: Deciding the main content to be represented in a sentence or the information to mention in the text. For instance, in the pollen example above, deciding whether to explicitly mention that pollen level is 7 in the south-east.
- Document structuring: Deciding the structure or organization of the conveyed information. For example, deciding to describe the areas with high pollen levels first, instead of the areas with low pollen levels.
- Aggregation: Putting of similar sentences together to improve understanding and readability. For instance, merging the two sentences Grass pollen levels for Friday have increased from the moderate to high levels of yesterday and Grass pollen levels will be around 6 to 7 across most parts of the country into the single sentence Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country.
- Lexical choice: Using appropriate words that convey the meaning clearly. For example, deciding whether medium or moderate should be used when describing a pollen level of 4.
- Referring expression generation: Creating such referral expressions that help in identification of a particular object and region. For example, deciding to use in the Northern Isles and far northeast of mainland Scotland to refer to a certain region in Scotland. This task also includes making decisions about pronouns and other types of anaphora.
- Realisation: Creating and optimizing the text that should be correct as per the rules of grammar. For example, using will be for the future tense of to be.
Techniques for Evaluating NLG systems
- Task-based evaluation: It includes human-based evaluation, who assess how well it helps him perform a task. For example, a system which generates summaries of medical data can be evaluated by giving these summaries to doctors and assessing whether the summaries help doctors make better decisions.
- Human ratings: It assess the generated text on the basis of ratings given by a person on the quality and usefulness of the text.
- Metrics: It compares generated texts to texts written by professionals.
An example of an interactive use of natural language generation is the WYSIWYM framework, which stands for “What you see is what you meant ” It allows users to see and manipulate the continuously rendered view (NLG output) of an underlying formal language document (NLG input), thereby editing the formal language without learning it.
Another example includes Content generation systems that assist human writers and makes the writing process more efficient and effective. A content generation tool based on web mining using search engines APIs has been built. The tool imitates the cut-and-paste writing scenario where a writer forms its content from various search results.
So far, the most successful NLG applications have been Data-to-Text systems, which generate textual summaries of databases and data sets; these systems usually perform data analysis as well as text generation. In particular, several systems have been built that produce textual weather forecasts from weather data.
Applications of Natural Language Generation
Natural language generation, or NLG, has numerous important uses in a range of sectors. The following are a few notable domains where NLG is extensively employed
Intelligent Automation and Reporting:
- NLG is used to transform analytical and complex data into reports and summaries that are understandable to humans. This makes it especially easy for stakeholders to comprehend and act upon insights in business intelligence.
Marketing and Content Creation:
- NLG is used to create content for blogs, websites, and advertising collateral. It can produce written materials at scale, including product descriptions and promotional content.
Virtual assistants and chatbots:
- By allowing chatbots and virtual assistants to respond in natural language, natural language generation (NLG) improves their conversational skills. Ensuring a user experience that is both engaging and human-like is imperative.
Analysis of Finance and Investments:
- Using numerical data and trends, natural language generation (NGL) is used in the finance industry to automatically produce financial reports, investment summaries, and market commentary.
Medical Records:
- NLG is used to generate medical reports, documentation, and patient summaries from electronic health records (EHR). In medical settings, it can simplify the documentation procedure.
Educational Content and E-Learning:
- NLG contributes to the creation of instructional materials, assessments, and personalised feedback for students. It aids in the creation of learning platforms that are adaptable.
Differences between NLP, NLG, and NLU
Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU) are three distinct but linked areas of natural language processing. Here’s a brief overview of the differences between them:
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Natural Language Processing |
Natural Language Generation |
Natural Language Understanding |
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|---|---|---|---|
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Definition |
Natural Language Processing (NLP) is a large scientific field that studies how human language and computers interact. It includes all activities about the comprehension, interpretation, and production of spoken language. |
NLG is a subset of NLP that focuses on computer-generated language that resembles that of humans. It entails transforming information or structured data into text written in natural language. |
NLU is a subset of NLP that is primarily concerned with how computers understand and interpret human language. It entails deriving meaning from textual information. |
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Objectives |
The goal of natural language processing (NLP) is to make it possible for computers to comprehend, interpret, and produce meaningful, contextually relevant human language. |
The goal of natural language generation (NLG) is to produce text that is logical, appropriate for the context, and sounds like human speech. Applications where the objective is to generate reports, summaries, or content that is readable by humans frequently use it. |
NLU seeks to give machines the ability to comprehend the meaning, context, and intent of human language. This covers tasks like sentiment analysis, language comprehension, and entity recognition. |
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Applications |
Natural language processing (NLP) finds application in a multitude of fields, such as speech recognition, machine translation, sentiment analysis, and information retrieval. |
Natural language generation (NLG) is used in chatbots, content production, automated report generation, and any other situation that calls for the conversion of structured data into natural language text. |
Natural language understanding (NLU) is essential for systems that need to extract insights and information from text data, such as chatbots and virtual assistants. |
Frequently Asked Questions (FAQs)
Q. What does “natural language generation” mean?
The process of using artificial intelligence to convert data into natural language is known as natural language generation, or NLG. NLG software accomplishes this by converting numbers into human-readable natural language text or speech using artificial intelligence models driven by machine learning and deep learning.
Q. What distinguishes NLG from NLP and NLU?
Natural language processing, or NLP, is a more general field that studies how computers and human language interact. While NLU (Natural Language Understanding) is concerned with understanding and deriving meaning from language, NLG is focused on text generation.
Q. What are the key components of NLG?
Sentence creation, refinement, content planning, and text planning are all common NLG tasks. By taking these precautions, the generated text is guaranteed to be grammatically correct, contextually relevant, and compliant.
Q. What makes NLG significant?
Natural language generation is included in many business intelligence (BI) tools because it can be helpful in situations where text-based narratives or spoken content need to be generated from business data. The most popular use of NLG is as a practical addition to self-service analysis.