From 5ead5f688b43da4404d00ffe18b216f64243c0cd Mon Sep 17 00:00:00 2001 From: Said Bleik Date: Mon, 19 Aug 2019 18:36:39 -0400 Subject: [PATCH] minor --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 97140ef..8ccfc44 100755 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ The following is a summary of the commonly used NLP scenarios covered in the rep |Named Entity Recognition |BERT| Named Entity Recognition (NER) is the task of classifying words or key phrases of a text into predefined entities of interest. | |Entailment |BERT| Textual entailment is a binary relation between two natural-language texts (called ‘text’ and ‘hypothesis’), where readers of the ‘text’ would agree the ‘hypothesis’ is most likely true. | |Question Answering |BiDAF
BERT| Question Answering (QA) is the task of retrieving or generating a valid answer for a given natural language query. | -|Sentence Similarity |Representation: TF-IDF, Word Embeddings, Doc Embeddings
Metrics: Cosine Similarity, Word Mover's Distance
BERT
GenSen| Sentence similarity is the process of computing a similarity score given a pair of text documents. | +|Sentence Similarity |Representation: TF-IDF, Word Embeddings, Doc Embeddings
Metrics: Cosine Similarity, Word Mover's Distance
Models: BERT, GenSen| Sentence similarity is the process of computing a similarity score given a pair of text documents. | |Embeddings| Word2Vec
fastText
GloVe| An embedding is a low dimensionality representation of the text that will be analyzed.