Inductive Learning

• Analyzing words into their linguistic components (morphemes).

• Morphemes are the smallest meaningful units of language.

• Ambiguity: More than one alternatives

flies flyVERB+PROG

flyNOUN+PLU

adam adam+ACC - the man (accusative)

adam+P1SG - my man

ada+P1SG+ACC - my island (accusative)

Parts-of-Speech (POS) Tagging

• Each word has a part-of-speech tag to describe its category.

• Part-of-speech tag of a word is one of major word groups (or its subgroups).

– open classes -- noun, verb, adjective, adverb

– closed classes -- prepositions, determiners, conjuctions, pronouns, particples

• POS Taggers try to find POS tags for the words.

• duck is a verb or noun? (morphological analyzer cannot make decision).

• A POS tagger may make that decision by looking the surrounding words.

– Duck! (verb)

– Duck is delicious for dinner. (noun)

Lexical Processing

• The purpose of lexical processing is to determine meanings of individual words.

• Basic methods is to lookup in a database of meanings – lexicon

• We should also identify non-words such as punctuation marks.

• Word-level ambiguity -- words may have several meanings, and the correct one

cannot be chosen based solely on the word itself.

– bank in English

• Solution -- resolve the ambiguity on the spot by POS tagging (if possible) or passon

the ambiguity to the other levels.

Syntactic Processing

• Parsing -- converting a flat input sentence into a hierarchical structure that

corresponds to the units of meaning in the sentence.

• There are different parsing formalisms and algorithms.

• Most formalisms have two main components:

– grammar -- a declarative representation describing the syntactic structure

of sentences in the language.

– parser -- an algorithm that analyzes the input and outputs its structural

representation (its parse) consistent with the grammar specification.

• CFGs are in the center of many of the parsing mechanisms. But they are

complemented by some additional features that make the formalism more suitable

to handle natural languages.

Semantic Analysis

• Assigning meanings to the structures created by syntactic analysis.

• Mapping words and structures to particular domain objects in way consistent with

our knowledge of the world.

• Semantic can play an import role in selecting among competing syntactic analyses

and discarding illogical analyses.

– I robbed the bank -- bank is a river bank or a financial institution

• We have to decide the formalisms which will be used in the meaning

representation.

Knowledge Representation for NLP

• Which knowledge representation will be used depends on the application --

Machine Translation, Database Query System.

• Requires the choice of representational framework, as well as the specific

meaning vocabulary (what are concepts and relationship between these concepts

-- ontology)

• Must be computationally effective.

• Common representational formalisms:

– first order predicate logic

– conceptual dependency graphs

– semantic networks

– Frame-based representations