Context Filter

The sheer size of Quotebank makes reductions unavoidable. We filter:

  1. Quotes that are not useful for our purposes: Speaker can’t be identified with certainty
  2. Columns: phase and probas (after identifying the speaker)
  3. Off-topic quotes

While the first two points can be trivially executed, the third criteria requires more care. In the following we will briefly outline how we extracted the semantics from quotes to filter off-topic quotes.


The selection of the quotes that are relevant to the topic of financial markets is done using vector representations of words, in particular we employ fastText , a state-of-the-art model for text representations. We tokenize the data and convert to lower case, removing any english stopwords. We then train the model using the standard unsupervised fastText. This way, each quote is transformed into a set of word embeddings. We define a set of keywords that we consider representative of the topic we are analyzing, i.e.[stockmarket, stock, bonds, shares, obligations, finance] and compute the mean vector representation of each word. We then compare the embedding of each word in a quote to the computed key word vector and choose the closest distance as representative value for similarity of the whole quote. We filter quotes by some threshold of similarity to reduce data.