Ling Luo 2015-08-31

Works in the past:

1.Finish training word embeddings via 5 models : using EnWiki dataset(953M): CBOW,Skip-Gram using text8 dataset(95.3M): CBOW,Skip-Gram,C&W,GloVe,LBL and Order(count-based)

2.Use tasks to measure quality of the word vectors with various dimensions(10~200): word similarity(ws) the TOEFL set:small dataset analogy task:9K semantic and 10.5K syntactic analogy questions text classification:IMDB dataset——pos&neg,use unlabeled dataset to train word embeddings sentence-level sentiment classification (based on convolutional neural networks) part-of-speech tagging

Works in this week:

word similarity(ws): try to use different similarity calculation method

named entity recognition(ner)

focus on cnn