Removed clothing_automl.xlsx
@@ -54,7 +54,7 @@ The following is a summary of the commonly used NLP scenarios covered in the rep
|Question Answering |BiDAF <br> BERT| Question answering (QA) is the task of retrieving or generating a valid answer for a given query in natural language, provided with a passage related to the query. |English|
|Sentence Similarity |Representation: TF-IDF, Word Embeddings, Doc Embeddings<br>Metrics: Cosine Similarity, Word Mover's Distance<br>Models: BERT, GenSen| Sentence similarity is the process of computing a similarity score given a pair of text documents. |English|
|Embeddings| Word2Vec<br>fastText<br>GloVe| Embedding is the process of converting a word or a piece of text to a continuous vector space of real number, usually, in low dimension.|English|
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+|Sentiment Analysis| Dependency Parser <br>GloVe| Provides an example of train and use Aspect Based Sentiment Analysis with Azure ML and [Intel NLP Architect](http://nlp_architect.nervanasys.com/absa.html) .|English|
## Getting Started
While solving NLP problems, it is always good to start with the prebuilt [Cognitive Services](https://azure.microsoft.com/en-us/services/cognitive-services/directory/lang/). When the needs are beyond the bounds of the prebuilt cognitive service and when you want to search for custom machine learning methods, you will find this repository very useful. To get started, navigate to the [Setup Guide](SETUP.md), which lists instructions on how to setup your environment and dependencies.
