MOSIUOA WESI NLP UNIT 6 THIRD SEMSTER AUCE

The final unit focuses on machine translation approaches including rule-based, statistical, and example-based methods. It explains evaluation metrics like BLEU, METEOR, and TER, and covers discourse phenomena like ellipsis and anaphora in translation. It also introduces real-world NLP applications such as text generation and dialogue systems.

1. MOSIUOA WESI – ANDHRA UNIVERSITY – VISAKHAPATNAM 530001

Unit VI: Natural Language Generation
and Applications
1. Introduction to Natural Language Generation (NLG)
Natural Language Generation (NLG) is the process of automatically producing natural
language text from structured or unstructured data. It is a subfield of Natural Language
Processing (NLP) that focuses on how machines can express information in human-
readable form. While Natural Language Understanding (NLU) deals with interpreting
language, NLG is about producing it.
2. Architecture of NLG Systems
An NLG system typically involves several stages:
- Content Determination: Selecting relevant information to express.
- Text Planning: Structuring and organizing information.
- Sentence Planning: Deciding on phrasing, referring expressions, and discourse markers.
- Surface Realization: Generating the final text output.

This pipeline ensures that generated text is coherent, fluent, and contextually appropriate.
3. Content Determination and Text Planning
Content determination identifies what information should be conveyed based on data, user
needs, or goals. Text planning organizes this content into a logical structure, often
represented as a document plan or tree structure. This stage ensures that the output text
follows a natural and meaningful order.
4. Sentence Planning and Surface Realization
Sentence planning involves selecting appropriate syntactic structures, referring
expressions, and lexical choices. Surface realization transforms these plans into
grammatically correct sentences. Techniques for surface realization range from template-
based approaches to neural language models.

2. 5. Techniques for NLG
NLG can be implemented using different approaches:
- Template-based generation: predefined sentence templates filled with data.
- Rule-based generation: grammar rules and linguistic knowledge.
- Statistical methods: probabilistic models to generate fluent text.
- Neural generation: large language models capable of producing coherent and context-
aware outputs.
6. Evaluation of NLG Systems
Evaluation of NLG focuses on both intrinsic and extrinsic measures:
- Fluency: how natural and grammatically correct the output is.
- Coherence: how logically structured the text is.
- Adequacy: how well the generated text conveys the intended meaning.
- BLEU, ROUGE, METEOR: common automatic evaluation metrics.
- Human evaluation for contextual relevance and quality.
7. Applications of NLG
NLG is widely applied in various domains:
- Automatic report generation (e.g., weather, finance, healthcare)
- Dialogue systems and chatbots
- Content creation and summarization
- Personalized recommendations and narratives
- Real-time data interpretation
8. Relation to NLP and AI
NLG plays a complementary role to NLU in building intelligent systems. Together, they form
the basis of advanced conversational agents, virtual assistants, and language-driven AI
applications. Modern NLG often leverages deep learning and large pre-trained language
models.
9. Ethical Considerations in NLG
Ethical concerns in NLG include:
- Bias in generated text due to biased training data.
- Misinformation or overgeneration of facts.
- Misuse for disinformation or spam.
- Privacy concerns in personalized content generation.
Ensuring transparency, accountability, and fairness is essential.

3. 10. Summary
Natural Language Generation is a crucial component of NLP that enables machines to
communicate effectively with humans. Through stages like content determination, sentence
planning, and surface realization, NLG systems generate fluent and meaningful text. Its
applications span industries, powering modern AI-driven communication and automation.