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• Module #1, Lecture #

Recap from the Previous Lecture

• Course Handout Discussion
• Project Groups

What is NLP?

● Branch of Artificial Intelligence

● Combination of linguistics (understanding how languages work) and computer science (building systems to solve natural language- related problems).

History of NLP

Georgetown Experiment

● Demonstration of machine translation performed by IBM at Georgetown University in 1954. ● Involved translation of 60+ sentences from Russian to English. ● Consisted of 6 grammar rules and 250 lexical items (stems + endings) ● Initially, it led to lots of research money to be used by governments for research in MT and NLP. However, real progress was much slower!

Syntactic Structures and Conceptual Dependency

Theory

● In 1957, Noam Chomsky came up with Syntactic Structures, which revolutionized linguistics and grammar. ○ Chomsky used phrase structure rules to generate new sentences. ○ Gave examples of grammatically correct sentences without any meaning. Example: “Colourless green ideas sleep furiously” ○ Advocated a separation of syntax from semantics

From Rules to Data

● Starting from the 1980s, we have seen a movement from using rule- based NLP systems to statistical systems due to the presence of data. ● With data, we can use probability theory to build reasonably robust systems for language modeling, machine translation, etc. ○ Example: Which one is correct in each pair and why? ■ I saw an elephant. Vs. I saw an equipment. ■ An European war is currently going on. Vs. A European war is currently going on. ■ Tell me something. Vs. Say me something. ● All this is possible because of probability.

Example of Machine Translation

● Earlier approach - Rule-based Machine Translation ● Linguists would create multiple rules in source language and target language. ● People would use dictionaries to map to root words, morphemes, etc. ● Limited in scope. Could not account for many challenges in MT.

Stages of NLP

Stages of Processing

● Phonetics and phonology ● Morphology ● Lexical Analysis ● Syntactic Analysis ● Semantic Analysis ● Pragmatics and Discourse

Morphology

● Word formation from root words and morphemes ○ Eg. singular - plural (teacher + s = teachers), gender (lion + ess = lioness), tense (listen + ing = listening), etc. ● First step in NLP - extract the morphemes of the given word ● Languages rich in morphology - Dravidian languages (Eg. Kannada, Tamil, Telugu, etc.) ○ Example: Maadidhanu - Maadu (root verb) + past tense + male singular ● Languages poor in morphology - English ○ Example: Did - Do (root verb) + past tense

Lexical Analysis

● Words have different meanings. ● Meanings have different words. Example: ● Where there’s a will… ● There are many relatives

Syntactic Analysis

• Consider the sentence “I like mangoes” S

NP

N V

N

VP

NP

mangoes

I like

Syntactic Analysis

● S -> NP VP

● NP -> N

● VP -> V NP

● N -> Noun (mangoes) / Pronoun (I) ● VP -> Verb (like)