Pasky’s Log

Guest post by Petr Marek (source)

Everybody driving a car needs the navigation to get to the destination fast and avoid traffic jam. One of the biggest problems is how to enter fast the destination and how to find where are the congestions, what is the traffic situation. YodaQA Traffic is a project attempting to answer the traffic related questions quickly and efficiently. Drivers may ask questions in natural language like: “What is the traffic situation in the Evropská street?” or “What is the fastest route from Opletalova street to Kafkova street?” You can try out the prototype (demo available only for limited time) – try to ask for example “traffic situation in the Wilsonova street” .

YodaQA Traffic still has some limitations. Currently we only have a browser version not suitable for smart phones. It is answering traffic questions for Prague’s streets only.

But as usual, this whole technology demo is open source – you can find it in the branch f/traffic-flow of our Hub project.

How does it work and where we get the data from?

All YodaQA are first analyzed to recognize and select traffic questions. We do it in two steps. The first step is to recognize the question topic. We use six topics like traffic situation, traffic incident or fastest route. The topic is determined by comparing semantic similarity of the user’s question with a set of reference questions. We estimate the similarity with our Dataset-STS Scoring API. Each reference question is labeled by a “topic”. The Sentence Pair Similarity algorithm selects the reference question “topic” with the highest similarity to the question.

Next we need to recognize the location, i.e. to recognize the street name. This is handled by another tool called the Label-lookup which we normally use for entity linking in YodaQA. It compares questions words with a list of all street names in the Prague. We exported the list of streets names in Prague from OpenStreetMap. We do not do exact match, we try to select the closest street name from the list.

The last step is to decide whether the question is really the traffic question, because the Dataset-STS API and Label-lookup can find topic and street name even in a pure movie question like “When was the Nightmare on Elm Street released?”. The Dataset-STS and Label-lookup return not only topic or street name but also the score, fortunately. We created dataset of over 70 traffic questions and over 300 movies questions and founded the minimal score thresholds, with which the recognition makes the lowest classification error on this dataset.

Once we know the type of question and the location we start a small script accessing the traffic situation data from HERE Maps. The only complication is that the the API doesn’t return traffic situation for particular street, but bounding box only. To overcome this problem we have to find a bounding box for a desired location, using an algorithm we developed for this purpose. Then we call the traffic flow API to acquire the information for all streets in the bounding box. Finally, we filter out the traffic situation for the desired street.

It was great fun to work on this application, it is not perfect but it shows how to create intelligent assistants helping people solving various everyday situations. We are also excited to see, how the users will use the new functionality of YodaQA and how it will help them.

Guest post by Petr Marek (source)

YodaQA learned how to use twitter during easter holidays. You can ask it by sending tweet with question to @askYodaQA . YodaQA will answer you shortly. How is it possible? I created app in the Google’s App Script, which handles receiving question from twitter and answering them.

Why did I create it? YodaQA can reach more users in the new interesting form thanks to it. I believe they will help us to find even more ways how YodaQA can help them. It is pretty symbiosis. YodaQA will help twitter users, and they will help it back. Let’s look how it is made.

The two important tools

The most important ingredient was Google’s App Script. It is basically JavaScript with the connection to Google services. You can make your own App Script apps in Google Drive. The best thing is that you can make triggers run the app every minute for example. And it’s for free.

The second thing you need is to create twitter app on account, which your bot will use to communicate with its followers. It will grant you access tokens, which you need to connect to twitter API. I used Twitter Lib for Google Apps Script to simplify the communication with API. It allowed me to tweet and get tweets with questions easily. You just need to call the right function with some arguments.

General idea behind

That was the tools that I used. But how did I make it work? I will describe the general idea now. I set App Script project to run my code every minute. The code does basically two things.

The first step is to obtain answers from twitter and to ask YodaQA. Bot searches all tweets with @askYodaQA. It saves the users that tweeted them and the time when it found the tweets. Then it sends the text of the tweet to YodaQA. YodaQA replies with dialog id and question id, which it saves to the list of questions.

The second step is to go through list of questions and to ask YodaQA for answers to these questions. Bot sends questions to users as soon as the answers are finished. You can even rely on features of the Hub, such as dialogs and coreference resolution. Two questions are connected to dialog when they are asked within five minutes interval.

I said that I save some information. Where? I used spreadsheet as memory. I use one sheet as “user memory”, the second as “asked question list” and the last as memory for the id of the last served tweet. I even log some information into a Google Docs text document. It may sound simple (and it is simple), but it works.

You can try it right now. Just tweet question with @askYodaQA and answer will arrive within few minutes. You can even use hashtags or mention other users. They will also receive the answer.

You can see the whole code on GitHub. You can use it and modify it for your own twitter bots too, maybe on your own data?