Since the advent of word2vec, word embeddings have become a go to method for encapsulating distributional semantics in NLP applications. This presentation will review the strengths and weaknesses of using pre-trained word embeddings, and demonstrate how to incorporate more complex semantic representation schemes such as Semantic Role Labeling, Abstract Meaning Representation and Semantic Dependency Parsing in to your applications.

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14. -
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18. Rob Speer
how-to-make-a-racist-ai-without-
really-trying.ipynb
From Agrawal et al.
Rob Speer
how-to-make-a-
racist-ai-without-
really-trying.ipynb

19. From Iyyer and collaborators.

20. Iyyer and collaborators broke the tree-structured
bidirectional LSTM sentiment classification model.

30. The tale of Mr. Morton gives a great intro to
Subject predicate structure. What ever the
predicate says he does.
Source https://people.eecs.berkeley.edu/~klein/cs294-7/SP07%20cs294%20lecture%2019%20--
%20compositional%20semantics%20(6pp).pdf and https://web.stanford.edu/~jurafsky/slp3/22.pdf

31. The tale of Mr. Morton gives a great intro to
Subject predicate structure. What ever the
predicate says he does.
Source https://people.eecs.berkeley.edu/~klein/cs294-7/SP07%20cs294%20lecture%2019%20--
%20compositional%20semantics%20(6pp).pdf and https://web.stanford.edu/~jurafsky/slp3/22.pdf

32. The tale of Mr. Morton gives a great intro to
Subject predicate structure. What ever the
predicate says he does.
Source https://people.eecs.berkeley.edu/~klein/cs294-7/SP07%20cs294%20lecture%2019%20--
%20compositional%20semantics%20(6pp).pdf and https://web.stanford.edu/~jurafsky/slp3/22.pdf

33. The tale of Mr. Morton gives a great intro to
Subject predicate structure. What ever the
predicate says he does.
Source https://people.eecs.berkeley.edu/~klein/cs294-7/SP07%20cs294%20lecture%2019%20--
%20compositional%20semantics%20(6pp).pdf and https://web.stanford.edu/~jurafsky/slp3/22.pdf

35. JAMR Parser

46. Prepare
Data
Register &
Manage Model
Train & Test
Model
Build
Image
…
Build model
(your favorite IDE)
Deploy Service &
Monitor Model
Prepare Experiment Deploy

49. https://azure.microsoft.com/en-us/services/virtual-
machines/data-science-virtual-machines/?WT.mc_id=beyondembeddings-
powerpoint-abornst
https://docs.microsoft.com/en-
us/python/api/overview/azure/ml/intro?view=azure-ml-
py?WT.mc_id=beyondembeddings-powerpoint-abornst
https://github.com/Azure/MachineLearningNotebooks
https://github.com/allenai/allennlp
https://github.com/aribornstein/pyNeurboParser