1. Introduction to Computational Linguistics Lecture 2

2. What is Computational Linguistics? CP: Computational Linguistics, NLP: Natural Language Processing, NLE: Natural Language Engineering, HLT: Human Language Technology etc. Formal definition: CL is a discipline between linguistics and computer science which is concerned with the computational aspects of the human language (Uszkreit, 2000).

3. Scientific and Technological Aspects of CL Human use natural language to communicate –Formal Theories –Linguistic Knowledge Is linguistic information helpful for doing Natural Language Processing? How machines can communicate with human –CL focuses on the practical outcome of modeling human language –The main obstacle between human and computer is communication –Computational Linguistics develop formal models to simulate human language technology and program them.

4. Linguistics Knowledge Focusing on Words Phonology: sounds (cats/dogz), homophones (bare/bear), rhythm (co`nvert, conve`rt) Morphology: related word forms (e.g., plural) Syntax: how to use the word in a sentence Lexical Meaning: meaning of words Compositional Semantics: the construction of complex words from the meaning of the parts (e.g., untruthfulness)

5. Words and Sentences Identification String x is a words in a text if and only if x is delimited by white space. In order to tell whether a string is a word, look it up in a dictionary. String w is a sentence in a text if and only if w starts with a capital letter and finishes with a full stop.

6. Example Next week, Mr. Ali will visit our department and he is planning to provide an amount of Rs. 12,000,000 to our bright students for their further studies. His company ‘XYZ’ has a huge name in the field of constructions. Plurals (chairs’)

7. Major Syntactic Constitutes Noun Phrase (NP): referring expressions Verb Phrase (VP): verbs plus complements Prepositional Phrase (PP): direction, location etc. Adjectival Phrase (AdjP): complemented adjectives Adverbial Phrase (AdvP): modified adjectives (very rapidly) Complementizers (Comp): (that, whether)

8. Examples The man saw a frog with a telescope The mouse was caught by the cat sat on the table.

11. Applications Automatic Tokenization Automatic Part of Speech Tagger Name Entity Recognition System Machine Translation –Word Sense Disambiguation –Example:River bank erosion is a growing problem. High-street bank is performing well to provide financial solutions. Question Answering System –QED: The Edinburgh TREC-2003 Question Answering System. Available at (www.iccs.informatics.ed.ac.uk/ ~s0239229/documents/Leidner- etal-2003-TREC.pdf) Query Searching (in search engines like Google)