JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech III Year NATURAL LANGUAGE PROCESSING Previous Question Paper (August - 2022 - R18)

Words are the smallest independent units of language, composed of smaller meaningful units called morphemes. The structure of words is analyzed through morphology, which examines how these morphemes combine.

The primary components of words are:

• Root (or Stem): The core part of the word that carries the primary lexical meaning and cannot be further subdivided. For example, in 'unhappiness', 'happy' is the root.
• Affixes: Morphemes that attach to the root to modify its meaning or grammatical function. These include prefixes (attached to the beginning, e.g., 'un-'), suffixes (attached to the end, e.g., '-ness'), infixes (inserted within the root), and circumfixes (surrounding the root).
• Bound vs. Free Morphemes: Free morphemes can stand alone as words (e.g., 'cat'), while bound morphemes must be attached to another morpheme (e.g., '-s' in 'cats').

Morphological processes such as derivation (creating new words, often changing the part of speech) and inflection (modifying a word to express grammatical categories like tense or number) govern how these components assemble.

The Finite State Transducer (FST) model is a widely used morphological model in natural language processing. It represents the relationship between the lexical level (the abstract representation of a word) and the surface level (the actual written form).

Key aspects of the FST model include:

• Mechanism: An FST is an extension of a Finite State Automaton that maps input strings to output strings. It processes a sequence of characters and produces a corresponding morphological analysis.
• Composition: FSTs can be composed to handle complex morphological rules. For example, one transducer can handle the mapping of the root, while another handles the phonetic changes (morphophonology) that occur when an affix is added.
• Bidirectionality: A significant advantage of FSTs is their bidirectional nature. The same model can be used for both morphological analysis (parsing a surface word into its components) and morphological generation (constructing a surface word from its components).
• Efficiency: Because FSTs are based on finite-state technology, they are computationally efficient, allowing for fast lookups and processing of large lexicons.

By defining states and transitions that represent valid morphological combinations, the FST model effectively captures the rules of word formation in a language.