Experiences from FastText in a text classification project.

FastText in a text classification project

In this blog I describe how we did text classification for funding applications with FastText package.

Describing the business need for text mining

Companies applied funding with this kind of form.

Application form could have been something like this. The form is a simplified example.
Application form could have been something like this. The form is a simplified example.

 

The documents were classified in a several categories by the application handler in the process management software.

The handler classified the application document in several categories base on application texts.
The human handler classified the application to categories such as Business development, Agriculture and Digitalization.

 

The manual process was not only time consuming, but also frustrating. Reporting was the primary reason for the classification.

Text data is often the most sensitive data

We had two primary ways of getting data.

Customer’s software was developed by Solita’s team. This made it easy to access the SQL database of the testing environment. As a result, we had all numerical and structured data in our hands. Numerical data was useful for application risk prediction, but we needed text data for document classification.

The text data was encrypted in the test database. This meant that we needed a way to securely import the plain language text data from the production SQL database.

There is a good reason why the access to text data should not be easy. Text data might contain sensitive information such as personal data or business secrets.

Selecting FastText as our text mining tool

My personal experience from text mining and classification was very thin. After discussions with the team we decided to go with the FastText package. It has been designed for simple text classification by Facebook.

FastText is quite easy command line tool for both supervised and unsupervised learning. We used a python package which apparently don’t support all original features such as nearest neighbor prediction [link].

For supervised prediction you create individual text files for training and testing data [link]. After files are created, training the neural network behind FastText takes just a few lines of code. We used the supervised method to classify the applications.

Example from FastText supervised tutorial data. FastText training data has labels at the beginning of each line followed by the actual text.
FastText training data has labels at the beginning of each line followed by the actual text.

 

For unsupervised analysis you can just dump a bunch of text to a file to create word vectors [link]. Word vectors are useful for finding words similar to each other.

While English has either singular or plural format such as dog or dogs, Finnish language has koira, koirat, koirani, koiranne, koirienne, koirilatammekohan… There are literally tens of variations for each word. FastText is especially great for languages like Finnish where suffixes at the end of each word vary depending on the context. This is because in addition of creating features from word counts FastText can also take into account combinations of words as well as sub-word character sequences.

A model per category using a document as an observation

Each application had multiple text fields and multiple categories to automatically predict. How to approach the complex problem?

In database the there was individual row for each combination of application and text field.
In database the there was a row for each combination of application and text field.

 

We decided to bundle all applicable text fields from the applications together. Another option would have been to make predictions for each combination of application and text field, and then select the class with most “votes” from text field predictions.

We combined all answers to a single string and make one prediction per application.
We combined all answers to a single string and did one prediction per application.

 

We left out text fields such as team description. Those fields did not bring significant information for the classification.

Trying to understand the labeling principles of FastText made us scratch our heads. The initial idea was to create a single classification model. That model would have included all related labels in a single training row.

In theory this could lead to a situation where all top predictions are from the same category such as Digitalization. As we wanted to get the most probable prediction from each category, we decided to train individual model per category (Business development, Agriculture and Digitalization).

FastText supervised algorithm accuracy

The labels inside categories were unequally balanced. Some categories had even tens of labels with very few observations.

Example of label count shares for Digitalization category.
Example of label count shares for Digitalization category.

 

Class imbalance meant that prediction accuracy reached 50% to 90% for some categories by simply guessing the most frequent label. We took this as our base line.

Eventually our model-per-category-strategy produced a few percentage units higher accuracies than choosing the most common label. This only happened after we decided to return the weakest predictions back to manual processing. The probability of prediction’s correctness was automatically given by FastText.

In our case it was enough to beat the naive strategy of choosing the most common label.

The prediction ability of FastText increases when applications with low prediction probability are returned to manual classification.
The prediction ability of FastText increases when applications with low prediction probability are returned to manual classification. This decreases the number of applications getting automated prediction.

Summarizing the FastText classification experiment

Apparently the application handlers don’t pay too much attention about which label they choose. This made us question the whole process. What is the value of reports that are based on application handler’s hunch? And if the labeling criteria are not uniform, how could a machine find any patterns?

Let’s say there are 2000 annual applications. One of the labels gets selected 30 times per year. Binomial probability calculation reveals that 95% confidence interval for 30 labels is actually from 20 to 40. A decision maker might think that a series of 20, 30 and 40 during a three year range indicates ascending trend for the label. But in reality, it’s just a matter of random variation. In one of the categories 15 out of 20 labels had this few or less observations.

FastText favored more common labels as it increased the overall accuracy. This came with the cost that some labels never got predictions.

When the solution has ran in production for a while, it is time to see if the handlers ever make the effort to correct the machine’s initial recommendation. If not, some labels will never end up to the reports.

There are endless number of solutions to automate such document classification. In our project the fast testing cycle to try different approaches was the key. The goal was not to make perfect, but improve the existing situation.

Whatever the prediction accuracy will be, this kind of text mining experiment provides valuable information for the organization.

A Data scientist’s abc to AI ethics, part 1 – About AI and ethics

In this series of posts I’ll try to paint the borderline between AI and ethics from a bit more analytical and technically oriented perspective. My immediate aims are to restrict hype meanings, and to draw some links to related fields.

In the daily life we constantly encounter new types of machine actors: in social media; in the grocery store; when negotiating a loan. Some of them appear amiable and friendly, but almost all are difficult to understand deeply. Their constitution may be cryptic and inaccessible.

It’s of course a subject of interest as in which ways algorithms and machine actors impact our society. Maybe they do not remain value-free or neutral in a larger context.

Philosophical and other types of interest

The Finnish Philosophical Society’s January 2019 colloquium targeted these kinds of questions. Talks concerned AI, humanity, and society at large. Prominent topics included the existence of machine autonomy and ethics. One interesting track concerned the definitions of moral and juridic responsibility. Many weighty concepts like humanity,  personhood, and the aesthetics of AI, were discussed too.

From a purely philosophical perspective, technology might be viewed as one particular type of otherness. It is something out of bounds of direct personal interest.

On the other hand the landscape around AI may appear supercharged at the moment. Even the word AI reveals many interest vectors. “Whose agenda does the ethics of AI in each case forward?” Maija-Riitta Ollila asked in her presentation.

No wonder many people with a technical background are a bit wary of the term. Often it would be more appropriate to use a less charged one – some good alternatives include machine learning, statistical analysis, and decision modeling.

Between AI and ethics

Most of the talks in the colloquium shared this very sensible view that AI as a term should be subject to critique. One moral responsibility then for tech people is just shooting down related hype.

But the landscape of AI and ethics is complex and controversial. As if to back this observation, many presenters in the colloquium openly asked the audience to correct them on technical points if they should go wrong.

For instance, cognitive and emotional modeling are named as two quite distinct areas of research within cognitive science and neuroscience. The first holds much more progress than the other, when we compare their achievements. Logic is relatively easier to simulate than emotional attitudes. We may equate this with the innate complexity of human action and information processing that this simulation platform only exemplifies.

Furthermore, as illustrated by many intriguing thought experiments, problems arise when we try to attribute an ethical or moral role to a machine actor. Some of these I’ll try to explicate in later posts.

Interests divide the world

A bit of a discomfort for me has been the relationship between AI discussion and ethics. Is the talk always morally sound? Sometimes it felt that ethics won’t fit into the world of AI marketing. If I should define ethics with a few words, I would probably state that it is deep thinking about prevalent problems of good and bad.

Some wisdom about AI

We may juxtapose this with a punchline about contemporary AI. “[The] systems are merely optimization machines, and ultimately, their target is optimization of business profit”, one fellow Data scientist wryly commented to me.

So on the surface level, computer science and mathematical problems might not connect to ethics at all. The situation may be alike in sales and marketing. Also in philosophy, formal logic on the one hand and ethics and cultural philosophy on the other are largely separate areas.

What to make of this divide? My next post will examine popular perceptions of AI in the wild.

This is the first of four posts that will handle the topics of AI and ethics from a bit more technical angle.

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