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Natural Language Computing

Tweet Classifier

  • Split the tweets into sentences
  • Tag them with a PoS tagger provided
  • Gather some feature information from each tweet
  • Use this information in machine learning classifiers that estimate the Twitter account (i.e., the twit) that produced a given tweet

Statistical Machine Translation

  • Build bigram and unigram models of English and French, to smooth their probabilities using add-δ discounting
  • Build a world-alignment model between English and French using the IBM-1 model
  • Use these models to translate French sentences into English with a decoder

Speech Recognition

  • Experiment with speaker identification by training mixtures of Gaussians to the acoustic characteristics of individual speakers
  • Identify unknown speakers based on these models.
  • Experiment with speech recognition by training hidden Markov models, and analyzing system’s performance with word-error rates

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