JNTU B.Tech III NATURAL LANGUAGE PROCESSING Previous Question Paper (March-2021 - R18)

Morphological models aim to analyze the internal structure of words. The primary challenges include:

• Morphotactics: Modeling the rules that govern the order in which morphemes can be combined to form valid words.
• Orthographic Rules: Handling spelling changes that occur when morphemes are concatenated, such as consonant doubling (e.g., 'run' to 'running') or vowel deletion.
• Ambiguity: Dealing with cases where a single word form can be parsed into multiple valid morphological structures, such as the word 'unlockable' meaning either 'able to be unlocked' or 'not able to be locked'.
• Productivity: Managing the ability of a language to create new words through derivation and compounding, which makes it difficult to maintain an exhaustive lexicon.
• Non-concatenative Morphology: Handling languages where word formation involves processes like infixing, reduplication, or templatic morphology (common in Semitic languages), which cannot be easily modeled by simple linear concatenation.

Natural Language Processing (NLP) is essential because it bridges the gap between human communication and computational understanding. Its importance lies in the vast amount of unstructured data generated daily, which requires automated processing to extract actionable insights.

The primary goals of NLP include:

• Syntactic Analysis: Determining the grammatical structure of sentences to understand the relationship between words.
• Semantic Understanding: Extracting the actual meaning of words and phrases, including resolving word sense disambiguation and identifying entities.
• Pragmatics and Discourse: Interpreting language in context, including coreference resolution and understanding the intent behind a speaker's utterance.
• Human-Computer Interaction: Enabling natural interfaces like chatbots, virtual assistants, and voice-activated systems to facilitate easier access to technology.
• Information Retrieval and Summarization: Efficiently organizing, searching, and condensing large volumes of text to improve information accessibility.
• Machine Translation: Breaking down language barriers by enabling accurate, context-aware translation between different human languages.