Minor Project repository - 6th sem
To develop a hybrid model for clinical paraphrase detection.
A bilateral multi-perspective matching (BiMPM) model is used. Given two sentences P and Q, the model first encodes them with a BiLSTM encoder. Next, the two encoded sentences are matched in two directions P against Q and Q against P . In each matching direction, each time step of one sentence is matched against all time steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fixed-length matching vector. Finally, based on the matching vector, a decision is made through a fully connected layer.
Attention layer extracts words that are important to the meaning of the sentence and aggregate the representation of those informative words to form a sentence vector. This Model ”attends” to important parts of the sentence.
The dataset consists of 5,801 sentence pairs. The average sentence length is 21, the shortest sentence has 7 words and the longest 36. 3,900 are labeled as being in the paraphrase relationship. Standard split of 4,076 training pairs (67.5 of which are paraphrases) and 1,725 test pairs (66.5 paraphrases) used.
Created a medical paraphrase corpus from the clinical notes in i2b2 dataset.
- Training Set : 150 pairs of sentences
- Test Set : 60 pairs of sentences
Glove : https://nlp.stanford.edu/projects/glove/
Word2vec : https://code.google.com/archive/p/word2vec/
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent