Spacy question answering

The source code for this blog post is written in Python and Kerasand is available on Github. An year or so ago, a chatbot named Eugene Goostman made it to the mainstream newsafter having been reported as the first computer program to have passed the famed Turing Test in an event organized at the University of Reading.

This leads us to the question: Is the Turing Test, in its original form, a suitable test for AI in the modern day? The task involves answering an open-ended question or a series of questions about an image. An example is shown below:. Since the problem cuts through two two very different modalities vision and textand requires high-level understanding of the scene, it appears to be an ideal candidate for a true Turing Test.

The problem also has real world applications, like helping the visually impaired. An important aspect of solving this problem is to have a system that can generate new answers. While most of the answers in the VQA dataset are short wordswe would still like to a have a system that can generate arbitrarily long answers, keeping up with our spirit of the Turing test. We can perhaps take inspiration from papers on Sequence to Sequence Learning using RNNsthat solve a similar problem when generating translations of arbitrary length.

Multi-word methods have been presented for VQA too.

spacy question answering

However, for the purpose of this blog post, we will ignore this aspect of the problem. We will select the most frequent answers in the VQA training dataset, and solve the problem in a multi-class classification setting.

An MLP is a simple feedforward neural net that maps a feature vector of fixed length to an appropriate output. In our problem, this output will be a probability distribution over the set of possible answers. We will be using Kerasan awesome deep learning library based on Theanoand written in Python. Setting up Keras is fairly easy, just have a look at their readme to get started. In order to use the MLP model, we need to map all our input questions and images to a feature vector of fixed length.

We perform the following operations to achieve this:. The following Keras code defines a multi-layer perceptron with two hidden layers, hidden units in each layer and dropout layers in the middle for regularization.

The final layer is a softmax layer, and is responsible for generating the probability distribution over the set of possible answers. The rmsprop method is used for optimzation. You can try experimenting with other optimizers, and see what kind of learning curves you get. Have a look at the entire python script to see the code for generating the features and training the network. You can reduce memory usage by lowering the batchSize variable, but that would also lead to longer training times.

I ran my experiments for epochs. A drawback of the previous approach is that we ignore the sequential nature of the questions. A way to tackle this limitation is by use of Recurrent Neural Networkswhich are well-suited for sequential data. You can also experiment with other recurrent layers in Keras, such as GRU. The word vectors corresponding to the tokens in the question are passed to an LSTM in a sequential fashion, and the output of the LSTM from its output gate after all the tokens have been passed is chosen as the representation for the entire question.

This fixed length vector is concatenated with the dimensional CNN vector for the image, and passed on to a multi-layer perceptron with fully connected layers. The last layer is once again softmax, and provides us with a probability distribution over the possible outputs. There has been a lot of discussion regarding training LSTMs with variable length sequences, and I used the following technique: Sorted all the questions by their length, and then processed them in batches of while training.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Learn more. Questions tagged [spacy]. Ask Question. Filter by. Sorted by. Tagged with. Apply filter. DataFrame [[12,"Excellent Oscar Salas 3 4 4 bronze badges. Initially I had installed the latest spacy version but was getting the following error during the training ValueError: [E] Trying to set Minses7 11 2 2 bronze badges.

I'm trying to train a model on my own data and I'm using Spacy library. But I'm confused about " index of token head" in a code example. Self-taught 15 4 4 bronze badges. How to fix MemoryError: Unable to allocate I'm able to create a conditional frequency distribution, convert it to the data frame and Gavrk 9 9 bronze badges.

How does spaCy generate vectors for phrases? Medium and large vocabularies of spaCy can generate vectors for words and phrases. Andrei 3 1 1 bronze badge. Just from a quick look I can identify some patterns in the product description such as: This product is red. This product is Vlad 33 1 1 silver badge 5 5 bronze badges. Matcher is Ethe99 41 6 6 bronze badges. I'm trying to estimate the cosine similarity between each document i in a Corpus A and all documents in a Corpus B.

Any idea how I can do this efficiently?

Building a Question-Answering System from Scratch— Part 1

I'm working with pretty large datasets. Vash 7 7 bronze badges. Akash Khairnar 1 1 1 bronze badge.This uses a classical CNN-LSTM model like shown below, where Image features and language features are computed separately and combined together and a multi-layer perceptron is trained on the combined features. Extracting image features involves, taking a raw image, and running it through the model, until we reach the last layer.

It is because the last layer of VGG Net is a way softmax and the second last layer is the Dropout. The question has to be converted into some form of word embeddings. Most popular is Word2Vec whereas these days state of the art uses skip-thought vectors or positional encodings.

We will use Word2Vec from Stanford called Glove. Glove reduces a given token into a dimensional representation. As we can see, obama and putin are very similar in representation than obama and banana. This shows you there is some semantic knowledge of the tokens embedded in the dimensional representation. See this blog post for more details. VQA is a simple model which combines features from Image and Word Embeddings and runs a multiple layer perceptron. As it can be seen above the model also runs a 3 layered LSTM on the word embeddings.

To get a naive result it is sufficient to feed the word embeddings directly to the merge layer, but as mentioned above the model gives close to the state-of-the-art results.

spacy question answering

Also, four layers of fully connected layers might not be required to achieve a good enough results. But I settled on this model after some experimentation, and this model's results beat those obtained using only few layers. I am copying the output of the previous command, so that you can validate if your results are same as mine.

Since cv2. Feel free to change that url to any valid image, it can be any image format. Also try to use websites which have higher bandwidth.

As you can see, it got this wrong, but you can see why it could be harder to guess soccer and easier to guess tennis, lack of soccer ball and double lines at the edge. This is an impertinent problem with classification tasks. Feel free to experiment with different types of questions, countcolorlocation.

More interesting results are obtained when one takes a different crop of a image, instead of just scaling it to x This is again because we extract only the top level features of CNN model which was trained to classify one object in the image.

Similar models have been presented at following links, this work takes ideas from them. Requires the file VGG. Let's do it! Thus we are extracting the Dimension image features from VGG Asketh Away! What vehicle is in the picture? What are they playing?

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Let's ask another question for the same image.Doing cool things with data! Learnt a whole bunch of new things. In this blog, I want to cover the main building blocks of a question answering model. You can find the full code on my Github repo. I have also recently added a web demo for this model where you can put in any paragraph and ask questions related to it. Check it out at link. SQuAD Dataset. S tanford Qu estion A nswering D ataset SQuAD is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or spanfrom the corresponding reading passage.

There has been a rapid progress on the SQuAD dataset with some of the latest models achieving human level accuracy in the task of question answering! Examples of context, question and answer on SQuAD. Context — Apollo ran from toand was supported by the two-man Gemini program which ran concurrently with it from to Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions.

Apollo used Saturn family rockets as launch vehicles. Question — What space station supported three manned missions in —? The training dataset for the model consists of context and corresponding questions. Both of these can be broken into individual words and then these words converted into Word Embeddings using pretrained vector like GloVe vectors. To learn more about Word Embeddings please check out this article from me. Word Embeddings are much better at capturing the context around the words than using a one hot vector for every word.

The next layer we add in the model is a RNN based Encoder layer. We would like each word in the context to be aware of words before it and after it. The output of the RNN is a series of hidden vectors in the forward and backward direction and we concatenate them.

Similarly we can use the same RNN Encoder to create question hidden vectors. Up til now we have a hidden vector for context and a hidden vector for question. To figure out the answer we need to look at the two together.

This is where attention comes in. Lets start with the simplest possible attention model:. Dot product attention. The dot product attention would be that for each context vector c i we multiply each question vector q j to get vector e i attention scores in the figure above. Softmax ensures that the sum of all e i is 1. Dot product attention is also described in the equations below. The above attention has been implemented as baseline attention in the Github code.

You can run the SQuAD model with the basic attention layer described above but the performance would not be good. More complex attention leads to much better performance. Lets describe the attention in the BiDAF paper. The main idea is that attention should flow both ways — from the context to the question and from the question to the context. Described in equation below:.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Objective Given an organisation's corpus of documents, generate a chatbot to enable natural question-answering capabilities. Demo - t.

[BERT] Pretranied Deep Bidirectional Transformers for Language Understanding (algorithm) - TDLS

Human Intervention If the search results are still not relevant, prompt a human to add the question-answer pair to the existing list of specified FAQs, or speak to a human. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.First part of the series focusses on Facebook Sentence Embedding.

With the help of my professors and discussions with the batch mates, I decided to build a question-answering model from scratch. The problem is pretty famous with all the big companies trying to jump up at the leaderboard and using advanced techniques like attention based RNN models to get the best accuracy.

However, my goal is not to reach the state of the art accuracy but to learn different NLP concepts, implement them and explore more solutions. I always believed in starting with basic models to know the baseline and this has been my approach here as well. This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems.

In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.

spacy question answering

All the codes can be found on this Github repository. I will give a brief overview, however, a detailed understanding of the problem can be found here. S tanford Qu estion A nswering D ataset SQuAD is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or spanfrom the corresponding reading passage.

For each observation in the training set, we have a context, question, and text. Example of one such observation. The goal is to find the text for any new question and context provided.

This is a closed dataset meaning that the answer to a question is always a part of the context and also a continuous span of context. I have broken this problem into two parts for now. These days we have all types of embeddings word2vecdoc2vecfood2vecnode2vecso why not sentence2vec. The basic idea behind all these embeddings is to use vectors of various dimensions to represent entities numerically, which makes it easier for computers to understand them for various downstream tasks.

Articles explaining these concepts are linked for your understanding. Traditionally, we used to average the vectors of all the words in a sentence called the bag of words approach. Each sentence is tokenized to words, vectors for these words can be found using glove embeddings and then take the average of all these vectors.

This technique has performed decently, but this is not a very accurate approach as it does not take care of the order of words. Here comes Infersentit is a sentence embeddings method that provides semantic sentence representations. It is trained on natural language inference data and generalizes well to many different tasks.

The process goes like this. Create a vocabulary from the training data and use this vocabulary to train infersent model. Once the model is trained, provide sentence as input to the encoder function which will return a dimensional vector irrespective of the number of words in the sentence. These embeddings can be used for various downstream tasks like finding similarity between two sentences.

I have implemented the same for Quora-Question Pair kaggle competition. You can check it out here. Coming to the SQuAD problem, below are the ways I have tried using sentence embedding to solve the first part of the problem described in the previous section.

I have further solved the problem using two major methods. Here, I first tried using euclidean distance to detect the sentence having minimum distance from the question.

This makes sense because euclidean distance does not care for alignment or angle between the vectors whereas cosine takes care of that. Direction is important in case of vectorial representations.Natural Language Processing and other AI technologies promise to let us build applications that offer smarter, more context-aware user experiences. However, an application that's almost smart is often very, very dumb.

In this tutorial, I'll show you how to set up a better brain for your applications — a Contextual Knowledge Base Graph. Applications feel particularly stupid when they make mistakes that a human never would, but which a human can sort of understand.

These mistakes reveal how crude the system's actual logic is, and the illusion that you're "talking" to something "intelligent" shatters.

To avoid these mistakes, we'd like our application to have a way to remember what the user has told it. We need to store these memories in a structured way — we want information we can act on, not just text we can search.

In this post, I'll show you how to start wiring up a solution to this problem, using free open-source technologies. Here's a sneak preview of what we're building:. The graph is contextual so that a query can automatically resolve into ground knowledge as a path in the graph. For example, a query "call John" will invoke the function "call", and a context "John". Before we can resolve these queries, we first have to build the CGKB. We want the knowledge base to be populated automatically.

We don't want the knowledge to be hard-coded by humans. Instead, the brain should learn by itself. So let's start setting it up. Note All code and examples presented in this tutorial are early implementations and still a work in progress.

Before you start, make sure you have the latest versions of Python and Node installed. We then install spaCy and Socket. IO using the Python package manager pip. We also have to download spaCy's statistical models about 1GB of data. Next, we need to install Neo4jthe graph database for our brain, with built in visualizer in the browser:. For the bot interface, install AIVAmy open-source framework for cross-plattform bot development.

Fork the repo and clone your fork locally:. It will ask you to change the password, use for this demo. The next step will depend on which platform you want to run your bot on. I prefer using Slackas it's generally easier. You're done! Now, go on Slack and talk to your bot. It should parse your input into its brain.

The demo essentially shows the syntactic dependency parse tree of your latest input. The NLP backend is powered by the node module spacy-nlp that connects to spaCy.


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