In this post I explain in detail the thought process behind designing a program for detecting news covering the same event. In case you are interested in simply implementing the solution as fast as possible, head over to the tutorial that I wrote and hopefuly give this post a read later on

During my previous internship, I spent a lot of time searching and perfecting a language independent algorithm that would filter out news articles covering the same event. In this blog post I will describe my search for that algorithm, including the process of testing potential solutions.

From the very beginning, it was clear to me that the final project will most likely consist of some sort of a similarity measure and an assessment of whether the similarity exceeds the maximum allowed threshold. In this article I will dissect my thought process, by first explaining my choice of similarity measurement (Jaccard vs TF—IDF and approach to comparing TF—IDF scores between articles) and then the rationale behind setting the similarity thresholds.

Jaccard Similarity of Sets

Initially I tried using Jaccard Similarity, where I would create a set of words from every article and compare similarity between these sets. Admittedly, there are better ways of creating the sets, such as ignoring stop words. However in this project I was unable to do that, due to the number of languages involved.

Initially this algorithm looked promising for my project, however after implementing and testing it, Jaccard Similarity did not provide the expected results. Its main flaw is not taking into account importance of the word in the text, thereby allowing common words to create excessive noise. Therefore, this approach left me searching for an algorithm, that would also assess the importance of the word.

TF-IDF

Term Frequency — Inverse Document Frequency or simply TF-IDF is a score that assesses the importance of a term (in this case — a word) in a document relative to a set of documents.

When using this method, the article comparison consists of two main parts: calculating the TF—IDF scores for words and comparing the scores between articles. The standard algorithm for comparison is cosine similarity and it works well for comparing the degree of similarity, but is resource expensive.

This method provides similarity of articles relative to the group that they belong to, therefore prior grouping of the articles can increase the accuracy of measurements. For example I conducted my first trial using a set of 200 documents written in approximately 10 different languages, as a result the similarity scores only predicted the language that the document belonged to and could vaguely indicate similar news topics. However, when I split the articles into language groups, I was able to set threshold for filtering out articles covering same events.

However, I believe that there’s room for performance improvement. Since we are exclusively concerned about whether the similarity exceeds the threshold, we can take advantage of this fact when labeling words as either important or not and thus take advantage of boolean operations in article comparison. I further discuss this idea under ‘Other Comparisons’ subsection, however I haven’t perfected the idea to a point where it would be superior to Cosine Comparison.

Calculating Weights

Calculating the importance of each word in a document, using TF—IDF weights, is straightforward and follows a simple formula:

\[ TFIDF = TF * IDF \]

Where:

W \( \leftarrow \) number of occurrences of a word in the article

N \(\leftarrow\) number of words in the article

Where:

O \(\leftarrow\) number of documents containing the word

D \(\leftarrow\) total number of documents

Cosine Comparison

When comparing TF—IDF scores of articles, cosine comparison is the standard approach and it is the one that I ended up using in my final implementation.

Formula for calculating similarity between vectors containing TF—IDF weights A and B is as follows:

Under the current implementation this formula can be nicely extended for entire dataframes.

\(A \leftarrow\) matrix containing TF—IDF scores, with each row belonging to a particular article and each column to a word.

\(n \leftarrow\) no. of words

Other Comparisons

While implementing the TF-IDF algorithm, I tried optimising the vector comparison, by marking a word as either important true or unimportant false. Then I would calculate the similarity by dividing the number of words marked as true in both the articles with the number of words marked as true in both articles.

Initially I thought that this might be a significantly more efficient approach than the cosine similarity, since the loop in which word values are marked as true or false has to run only once and boolean operations between words in articles are very efficient. Nevertheless, after a more careful consideration of the algorithm I realised that there are a couple of disadvantages to its efficiency. First, since comparison has to be made between all article pairs the growth rate ends up being \(n^2)\ just with a lower coefficient. Second, cosine similarity can be implemented in R using a couple of highly optimised dataframe operators, while my proposed algorithm presents an optimisation problem in itself. The bottom line being that while the method is promising, it presents optimisation problems too large to make it viable for this project.

Setting Thresholds

At this point I have chosen to use TF—IDF weights to assess the importance of words within articles and cosine similarity, to compare the ‘important words’ between articles. In this section I explain my thought process, when setting the cosine similarity values of TF—IDF weights, above which articles are labeled as duplicates.

Initially I tried comparing the articles by exclusively assessing the similarity scores within the main text, yet it wasn’t enough, since it would give unreasonably high similarity scores between events of the same type, with different subjects (two different stocks go up or two different football teams win).

Because of this, I decided to compare the similarity between titles first and then set different similarity thresholds for the full text. For my dataset, I’ve found a good threshold for labeling articles as repeats to be 0.2 full text similarity, when title similarity exceeds 0.2, full text similarity of 0.7, when title similarity is between 0.1 and 0.2 and 0.85 when title similarity is below 0.1.

This is the part of the process that will require the most fine—tuning, based on the desired balance between removing all repeating information and allowing the most unique items through.