TODO 6/6/15

• Train baseline and recurrent on 100k set to compare with recursive
• Train a separate model to predict relation/no relation and integrate with baseline and recurrent for kbp pipeline
• Generate plots from the data in rnn_xxx_dev_perf.log

5/26/15:

• Melvin: concat 2 entity vectors, predict softmax of relation at the top
• Melvin: avg of trained word vectors, predict relation at the top
• Ankur: recurrent NN, using dep path
• Mikhail: recursive NN (hw3 implementation); use NLTK to get constituency parse for sentences (but only keep the subtree that contains the mentions)

5/17/15:

• Compute word2vec for KBP corpus
• Keras - implement basic MLP with triples
• Keras - implement average of word vectors
• Keras - implement average of words in dependency path
• Figure out about the training and test data mismatch

5/10/15:

• mikhail: batch_size = 20, lr = 0.001, reg = 0.001
• ankur: batch_size = 20, lr = 0.01, reg = 0.001
• melvin: batch_size = 20, lr = 0.01, reg = 0.01
• train average model on 1 million sentences and test using validation set
• utilities to save and load a model

5/3/15:

• DL word vectors (glove)
• get code to extract dep path between mentions
• generate micro datasets in addition to the 10k (maybe 100k, 1mil)
• write a recursive NN

Desc of Training data CSV:

gloss | text | | extended | dependencies_conll | text | | extended | words | text[] | | extended | lemmas | text[] | | extended | pos_tags | text[] | | extended | ner_tags | text[] | | extended | subject_id | bigint | | plain | subject_entity | text | | extended | subject_link_score | real | | plain | subject_ner | text | | extended | object_id | bigint | | plain | object_entity | text | | extended | object_link_score | real | | plain | object_ner | text | | extended | subject_begin | smallint | | plain | subject_end | smallint | | plain | object_begin | smallint | | plain | object_end | smallint | | plain | known_relations | text[] | | extended | incompatible_relations | text[] | | extended | annotated_relation | text | | extended |

Desc of Test data CSV:

   Column       |   Type   | Modifiers | Storage  | Description 

--------------------+----------+-----------+----------+------------- gloss | text | | extended | dependencies_conll | text | | extended | words | text[] | | extended | lemmas | text[] | | extended | pos_tags | text[] | | extended | ner_tags | text[] | | extended | subject_id | bigint | | plain | subject_entity | text | | extended | subject_link_score | real | | plain | subject_ner | text | | extended | object_id | bigint | | plain | object_entity | text | | extended | object_link_score | real | | plain | object_ner | text | | extended | subject_begin | smallint | | plain | subject_end | smallint | | plain | object_begin | smallint | | plain | object_end | smallint | | plain |