Named-entity  recognition  (NER)  is  a  subtask  of  information  extraction  that  seeks  tolocate and classify named entity mentioned in unstructured text into pre-defined cate-gories such as person names, organizations, locations, medical codes, time expressions,quantities, monetary values, percentages, etc.

**Example:** An example tagging:

EU rejects German call to boycott British lamb .

EU/B-ORG rejects/O German/B-MISC call/O to/O boycott/O British/B-MISC lamb/O ./O


In this assignment, you will implement a NER tagger using Hidden Markov Models
(HMMs). Therefore, you will practice HMMs and Viterbi algorithm in this assignment.

## CHALLENGE

You can improve your NER tagger more to compete with other participants in the class. The accuracy of your NER tagger is the main criteria to be compared with others. The participants in the class that obtain a higher tagging accuracy will deserve more points.