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Knowledge Graphs and Neural Models: How NLP technologies are addressing fake news and scientific knowledge

A Practical Guide to Hybrid Natural Language Processing – Part 2 José Manuel Gómez Pérez, Ronald Denaux and Andrés García-Silva, all from the Expert System Artificial Intelligence (AI) Research Lab in Madrid, have just published “A Practical Guide to Hybrid Natural Language Processing.” The book is designed to be a reference on the possibilities offered…

L’articolo Knowledge Graphs and Neural Models: How NLP technologies are addressing fake news and scientific knowledge proviene da Expert System.

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A Practical Guide to Hybrid Natural Language Processing – Part 2

José Manuel Gómez Pérez, Ronald Denaux and Andrés García-Silva, all from the Expert System Artificial Intelligence (AI) Research Lab in Madrid, have just published “A Practical Guide to Hybrid Natural Language Processing.” The book is designed to be a reference on the possibilities offered by the intelligence use of current methods and tools in Natural Language Processing and Natural Language Understanding and on the short- and long-term challenges that users face as these technologies continue to evolve.

This is part 2 of our interview with one of the authors, José Manuel Gómez Pérez, Expert System R&D & International Projects Director.

What are some of the problems in detecting disinformation in text?

One challenge is the use of ordinary language or terminology as code, which enables members of a group to communicate on platforms like Twitter. Phrases like “snowflake” or “Karen” have political connotations, while “420” is an insider term for cannabis culture.

Such neologisms are not defined in any dictionary nor do they appear labeled in a dataset that we can use directly to train a model that allows us to identify them. In fact, there are practically no public datasets large enough and with the necessary quality in this area. Therefore, we would need to train the system using related tasks for which there is more labeled data available, such as false reviews about hotels, restaurants or celebrities, and then transfer the trained model to our domain. The combination of the representations learned in this way with neural representations of words and concepts extracted from a knowledge graph using the techniques that we describe in the book can significantly improve the results compared to approaches that do not apply this type of data augmentation.

What about fake news?

Fake news and disinformation pose their own challenges. One aspect of this is identifying whether a post on social media, for example, was posted by a human or a bot (since bots are more likely to be used to spread fake news) based solely on the content of the post. Another is verifying the information that appears in the post itself, which is the goal of the thousands of fact checkers around the world who are engaged in such activities today.

In both cases, the shortage of sufficiently large, labeled datasets in various languages ​​and themes (currently, most deal with political issues and are in English) is a problem. In addition, in order to understand how disinformation spreads and explain what it consists of and why our system reaches a certain conclusion, we need to combine neural models of language processing, which allow us to semantically compare a post with others that are already verified, with explicit representations of the sources of this information and the indicators that have led us to classify it as credible or not.

In the wake of COVID-19, being able to access information in scientific publications is especially valuable. What are the unique challenges here?

In this area, a classic problem in Artificial Intelligence is the development of systems capable of reading a scientific article or a chapter of a book in the same way a person would, assimilating the content that appears in the text, but also in the images, charts and diagrams it contains, so that the system is able to successfully answer questions asked about its content.

This presents several important challenges. First, scientific terminology is very broad, heterogeneous, and complex, with many terms that consist of multiple words. Furthermore, although the volume of scientific publications is virtually infinite, annotated datasets for tasks such as question answering related to the understanding of scientific texts are scarce.

We also need to be able to represent the information that appears in different data modalities to be able to reason with it in a homogeneous way. For example, we would need to be able to correlate a diagram that represents the process of photosynthesis, the text that describes it and its representation as a concept in a structured knowledge graph. It is necessary to look for alternative sources of supervision, such as the correspondence between a figure and the text contained in its footer.

This type of approach allows our models to learn characteristics associated with text and images that we can also enrich with representations of concepts extracted from scientific knowledge graphs. In the book, we demonstrate how to do this successfully in a way that improves the results compared to more conventional approaches.

What are the next challenges related to the interpretation of natural language?

In the last two or three years, the emergence of neural language models and, above all, those based on the Transformer architecture, such as BERT, GPT or RoBERTa, have revolutionized the NLP / NLU panorama, not only in the world of academia and research, but also in business. The way of solving problems related to text comprehension has changed radically thanks to the ability of these models and the cost savings that come from training NLP / NLU tasks in a specific domain without having to train from scratch, but by e fine-tuning the model on a much smaller subset of domain data than would previously have been necessary. In a very short time, most NLP / NLU systems will work on pre-trained Transformer-based language models.

However, we still have a lot to learn in order to understand exactly how these models represent the information contained in the language at a lexical, syntactic and semantic level. Without this knowledge, it is not possible to offer capabilities related to the explanation, justification or interpretation of the model’s results, which are critical in domains such as medicine, banking or insurance. In fact, it is extremely difficult for these models to interpret this knowledge logically, even more so to label it based on specific concepts or relationships, which on the other hand, turn out to be the main entities represented in a knowledge graph.

So, where should the research go?

In order to obtain significant advances in language comprehension by machines, it is necessary to enhance the incipient capacities of language models to reason about the text. For example, GPT-2 (now GPT-3 has just been released) is capable of inferring that a person’s native language from Italy is probably Italian or that a person from Madrid is Spanish. However, the model cannot calculate the square root of 2, nor does it know about the lethal effect of hydrochloric acid intake. Limitations like these prompt us to investigate how to merge the knowledge captured in language models with the symbolic knowledge contained in structured graphs.

In this sense, knowledge graphs such as ConceptNet or Atomic that focus on the representation of something as human as common sense, are of great interest. The progress in the development of complex reasoners based on the composition of different simpler models and the gradual creation of benchmarks oriented to their evaluation, such as DROP[1], are also promising.

In terms of R&D, what are the next challenges that your team will focus on?

Like I mentioned earlier, one of the main research questions that we ask ourselves is: Given a language model and a knowledge graph, is it possible to identify a correspondence between the representations of one and the other? Our results indicate that it is, and we address this in the book. The next step will be to create those mappings so that they can be exploited systematically and generate truly intelligent hybrid models that, paraphrasing Hiroaki Kitano, creator of the Robocup[2], may at some point make on their own Nobel Prize-worthy findings.

Read the first part of the interview here.

[1] https://allennlp.org/drop

[2] https://www.robocup.org/a_brief_history_of_robocup

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Graph Convolutional Networks (GCN)

In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it. Why Graphs? Many problems are graphs in true nature. In our world, we see many data are graphs, […]

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graph convolutional networks

In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it.

Why Graphs?

Many problems are graphs in true nature. In our world, we see many data are graphs, such as molecules, social networks, and paper citations networks.

Tasks on Graphs

  • Node classification: Predict a type of a given node
  • Link prediction: Predict whether two nodes are linked
  • Community detection: Identify densely linked clusters of nodes
  • Network similarity: How similar are two (sub)networks

Machine Learning Lifecycle

In the graph, we have node features (the data of nodes) and the structure of the graph (how nodes are connected).

For the former, we can easily get the data from each node. But when it comes to the structure, it is not trivial to extract useful information from it. For example, if 2 nodes are close to one another, should we treat them differently to other pairs? How about high and low degree nodes? In fact, each specific task can consume a lot of time and effort just for Feature Engineering, i.e., to distill the structure into our features.

graph convolutional network
Feature engineering on graphs. (Picture from [1])

It would be much better to somehow get both the node features and the structure as the input, and let the machine to figure out what information is useful by itself.

That’s why we need Graph Representation Learning.

graph convolutional network
We want the graph can learn the “feature engineering” by itself. (Picture from [1])

If this in-depth educational content on convolutional neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material. 

Graph Convolutional Networks (GCNs)

Paper: Semi-supervised Classification with Graph Convolutional Networks (2017) [3]

GCN is a type of convolutional neural network that can work directly on graphs and take advantage of their structural information.

it solves the problem of classifying nodes (such as documents) in a graph (such as a citation network), where labels are only available for a small subset of nodes (semi-supervised learning).

graph convolutional network
Example of Semi-supervised learning on Graphs. Some nodes dont have labels (unknown nodes).

Main Ideas

As the name “Convolutional” suggests, the idea was from Images and then brought to Graphs. However, when Images have a fixed structure, Graphs are much more complex.

graph convolutional network
Convolution idea from images to graphs. (Picture from [1])

The general idea of GCN: For each node, we get the feature information from all its neighbors and of course, the feature of itself. Assume we use the average() function. We will do the same for all the nodes. Finally, we feed these average values into a neural network.

In the following figure, we have a simple example with a citation network. Each node represents a research paper, while edges are the citations. We have a pre-process step here. Instead of using the raw papers as features, we convert the papers into vectors (by using NLP embedding, e.g., tf–idf).

Let’s consider the green node. First off, we get all the feature values of its neighbors, including itself, then take the average. The result will be passed through a neural network to return a resulting vector.

graph convolutional network
The main idea of GCN. Consider the green node. First, we take the average of all its neighbors, including itself. After that, the average value is passed through a neural network. Note that, in GCN, we simply use a fully connected layer. In this example, we get 2-dimension vectors as the output (2 nodes at the fully connected layer).

In practice, we can use more sophisticated aggregate functions rather than the average function. We can also stack more layers on top of each other to get a deeper GCN. The output of a layer will be treated as the input for the next layer.

graph convolutional network
Example of 2-layer GCN: The output of the first layer is the input of the second layer. Again, note that the neural network in GCN is simply a fully connected layer (Picture from [2])

Let’s take a closer look at the maths to see how it really works.

Intuition and the Maths behind

First, we need some notations

Let’s consider a graph G as below.

graph convolutional network
From the graph G, we have an adjacency matrix A and a Degree matrix D. We also have feature matrix X.

How can we get all the feature values from neighbors for each node? The solution lies in the multiplication of A and X.

Take a look at the first row of the adjacency matrix, we see that node A has a connection to E. The first row of the resulting matrix is the feature vector of E, which A connects to (Figure below). Similarly, the second row of the resulting matrix is the sum of feature vectors of D and E. By doing this, we can get the sum of all neighbors’ vectors.

graph convolutional network
Calculate the first row of the “sum vector matrix” AX
  • There are still some things that need to improve here.
  1. We miss the feature of the node itself. For example, the first row of the result matrix should contain features of node A too.
  2. Instead of sum() function, we need to take the average, or even better, the weighted average of neighbors’ feature vectors. Why don’t we use the sum() function? The reason is that when using the sum() function, high-degree nodes are likely to have huge v vectors, while low-degree nodes tend to get small aggregate vectors, which may later cause exploding or vanishing gradients (e.g., when using sigmoid). Besides, Neural networks seem to be sensitive to the scale of input data. Thus, we need to normalize these vectors to get rid of the potential issues.

In Problem (1), we can fix by adding an Identity matrix I to A to get a new adjacency matrix Ã.

Pick lambda = 1 (the feature of the node itself is just important as its neighbors), we have Ã = A + I. Note that we can treat lambda as a trainable parameter, but for now, just assign the lambda to 1, and even in the paper, lambda is just simply assigned to 1.

By adding a self-loop to each node, we have the new adjacency matrix

Problem (2)For matrix scaling, we usually multiply the matrix by a diagonal matrix. In this case, we want to take the average of the sum feature, or mathematically, to scale the sum vector matrix ÃX according to the node degrees. The gut feeling tells us that our diagonal matrix used to scale here is something related to the Degree matrix D̃ (Why , not D? Because we’re considering Degree matrix  of new adjacency matrix Ã, not A anymore).

The problem now becomes how we want to scale/normalize the sum vectors? In other words:

How we pass the information from neighbors to a specific node?

We would start with our old friend average. In this case, D̃ inverse (i.e., D̃^{-1}) comes into play. Basically, each element in D̃ inverse is the reciprocal of its corresponding term of the diagonal matrix D.

For example, node A has a degree of 2, so we multiple the sum vectors of node A by 1/2, while node E has a degree of 5, we should multiple the sum vector of E by 1/5, and so on.

Thus, by taking the multiplication of D̃ inverse and X, we can take the average of all neighbors’ feature vectors (including itself).

So far so good. But you may ask How about the weighted average()?. Intuitively, it should be better if we treat high and low degree nodes differently.

We’re just scaling by rows, but ignoring their corresponding columns (dash boxes)
Add a new scaler for columns.

The new scaler gives us the “weighted” average. What are we doing here is to put more weights on the nodes that have low-degree and reduce the impact of high-degree nodes. The idea of this weighted average is that we assume low-degree nodes would have bigger impacts on their neighbors, whereas high-degree nodes generate lower impacts as they scatter their influence at too many neighbors.

graph convolutional network
When aggregating feature at node B, we assign the biggest weight for node B itself (degree of 3), and the lowest weight for node E (degree of 5)
Because we normalize twice, we change “-1” to “-1/2”

For example, we have a multi-classification problem with 10 classes, F will be set to 10. After having the 10-dimension vectors at layer 2, we pass these vectors through a softmax function for the prediction.

The Loss function is simply calculated by the cross-entropy error over all labeled examples, where Y_{l} is the set of node indices that have labels.

The number of layers

The meaning of #layers

The number of layers is the farthest distance that node features can travel. For example, with 1 layer GCN, each node can only get the information from its neighbors. The gathering information process takes place independentlyat the same time for all the nodes.

When stacking another layer on top of the first one, we repeat the gathering info process, but this time, the neighbors already have information about their own neighbors (from the previous step). It makes the number of layers as the maximum number of hops that each node can travel. So, depends on how far we think a node should get information from the networks, we can config a proper number for #layers. But again, in the graph, normally we don’t want to go too far. With 6–7 hops, we almost get the entire graph which makes the aggregation less meaningful.

graph convolutional network
Example: Gathering info process with 2 layers of target node i

How many layers should we stack the GCN?

In the paper, the authors also conducted some experiments with shallow and deep GCNs. From the figure below, we see that the best results are obtained with a 2- or 3-layer model. Besides, with a deep GCN (more than 7 layers), it tends to get bad performances (dashed blue line). One solution is to use the residual connections between hidden layers (purple line).

graph convolutional network
Performance over #layers. Picture from the paper [3]

Take home notes

  • GCNs are used for semi-supervised learning on the graph.
  • GCNs use both node features and the structure for the training
  • The main idea of the GCN is to take the weighted average of all neighbors’ node features (including itself): Lower-degree nodes get larger weights. Then, we pass the resulting feature vectors through a neural network for training.
  • We can stack more layers to make GCNs deeper. Consider residual connections for deep GCNs. Normally, we go for 2 or 3-layer GCN.
  • Maths Note: When seeing a diagonal matrix, think of matrix scaling.
  • A demo for GCN with StellarGraph library here [5]. The library also provides many other algorithms for GNNs.

Note from the authors of the paper: The framework is currently limited to undirected graphs (weighted or unweighted). However, it is possible to handle both directed edges and edge features by representing the original directed graph as an undirected bipartite graph with additional nodes that represent edges in the original graph.

What’s next?

With GCNs, it seems we can make use of both the node features and the structure of the graph. However, what if the edges have different types? Should we treat each relationship differently? How to aggregate neighbors in this case? What are the advanced approaches recently?

In the next post of the graph topic, we will look into some more sophisticated methods.

graph convolutional network
How to deal with different relationships on the edges (brother, friend,….)?

REFERENCES

[1] Excellent slides on Graph Representation Learning by Jure Leskovec (Stanford):  https://drive.google.com/file/d/1By3udbOt10moIcSEgUQ0TR9twQX9Aq0G/view?usp=sharing

[2] Video Graph Convolutional Networks (GCNs) made simple: https://www.youtube.com/watch?v=2KRAOZIULzw

[3] Paper Semi-supervised Classification with Graph Convolutional Networks (2017): https://arxiv.org/pdf/1609.02907.pdf

[4] GCN source code: https://github.com/tkipf/gcn

[5] Demo with StellarGraph library: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-node-classification.html

This article was originally published on Medium and re-published to TOPBOTS with permission from the author.

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Microsoft BOT Framework — Loops

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Loops is one of the basic programming structure in any programming language. In this article, I would demonstrate Loops within Microsoft BOT framework.

To follow this article clearly, please have a quick read on the basics of the Microsoft BOT framework. I wrote a couple of articles sometime back and the links are below:

Let’s Get Started.

I would be using the example of a TaxiBot described in one of my previous article. The BOT asks some general questions and books a Taxi for the user. In this article, I would be providing an option to the user to choose there preferred cars for the ride. The flow will look like below:

Create a new Dialog Class for Loops

We would need 2 Dialog classes to be able to achieve this task:

  1. SuperTaxiBotDialog.cs: This would be the main dialog class. The waterfall will contains all the steps as defined in the previous article.
  2. ChooseCarDialog.cs: A new dialog class will be created which would allow the user to pick preferred cars. The loop will be defined in this class.

The water fall steps for both the classes could be visualized as:

The complete code base is present on the Github page.

Important Technical Aspects

  • Link between the Dialogs: In the constructor initialization of SuperTaxiBotDialog, add a dialog for ChooseCarDialog by adding the line:
AddDialog(new ChooseCarDialog());

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  • Call ChooseCarDialog from SuperTaxiBotDialog: SuperTaxiBotDialog calls ChooseCarDialog from the step SetPreferredCars, hence the return statement of the step should be like:
await stepContext.BeginDialogAsync(nameof(ChooseCarDialog), null, cancellationToken);
  • Return the flow back from ChooseCarDialog to SuperTaxiBotDialog: Once the user has selected 2 cars, the flow has to be sent back to SuperTaxiBotDialog from the step LoopCarAsync. This should be achieved by ending the ChooseCarDialog in the step LoopCarAsync.
return await stepContext.EndDialogAsync(carsSelected, cancellationToken);

The complete code base is present on the Github page.

Once the project is executed using BOT Framework Emulator, the output would look like:

Hopefully, this article will help the readers in implementing a loop with Microsoft BOT framework. For questions: Hit me.

Regards

Tarun

Source: https://chatbotslife.com/microsoft-bot-framework-loops-fe415f0e7ca1?source=rss—-a49517e4c30b—4

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The Bleeding Edge of Voice

This fall, a little known event is starting to make waves. As COVID dominates the headlines, an event called “Voice Launch” is pulling…

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Tapaan Chauhan

This fall, a little known event is starting to make waves. As COVID dominates the headlines, an event called “Voice Launch” is pulling together an impressive roster of start-ups and voice tech companies intending to uncover the next big ideas and start-ups in voice.

While voice tech has been around for a while, as the accuracy of speech recognition improves, it moves into its prime. “As speech recognition moves from 85% to 95% accuracy, who will use a keyboard anymore?” says Voice Launch organizer Eric Sauve. “And that new, more natural way to interact with our devices will usher in a series of technological advances,” he added.

Voice technology is something that has been dreamt of and worked on for decades all over the world. Why? Well, the answer is very straightforward. Voice recognition allows consumers to multitask by merely speaking to their Google Home, Amazon Alexa, Siri, etc. Digital voice recording works by recording a voice sample of a person’s speech and quickly converting it into written texts using machine language and sophisticated algorithms. Voice input is just the more efficient form of computing, says Mary Meeker in her ‘Annual Internet Trends Report.’ As a matter of fact, according to ComScore, 50% of all searches will be done by voice by 2020, and 30% of searches will be done without even a screen, according to Gartner. As voice becomes a part of things we use every day like our cars, phones, etc. it will become the new “norm.”

The event includes a number of inspiration sessions meant to help start-ups and founders pick the best strategies. Companies presenting here include industry leaders like Google and Amazon and less known hyper-growth voice tech companies like Deepgram and Balto and VCs like OMERS Ventures and Techstars.

But the focus of the event is the voice tech start-ups themselves, and this year’s event has some interesting participants. Start-ups will pitch their ideas, and the audience will vote to select the winners. The event is a cross between a standard pitchfest and Britain’s Got Talent.

Source: https://chatbotslife.com/the-bleeding-edge-of-voice-67538bd859a9?source=rss—-a49517e4c30b—4

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