Can I use Sentence-Bert to embed event triples?

External question — Data Science Stack Exchange Author: user900476 Original post: https://datascience.stackexchange.com/questions/111735 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I extracted event triples from sentences using OpenIE. Can I concatenate the components in the event triple to make it a sentence and use Sentence-Bert to embed it? It seems no one has done this way before so I am questioning my idea. I'm using news headlines to predict next day's stock movement. For example, there are two news headlines, the first is "U.S. stock index futures points to higher start" , I used openIE to extract it and there are two event triples, [('U.S. stock index futures', 'points to', 'start'), ('U.S. stock index futures', 'points to', 'higher start')]. (There are repetition in the openIE extracted event triples and I don't know how to avoid it.) Since it contains events I'm interested in (stock index), I will embed these two events and take their mean as the the embedding. The second headline is "STOCKS NEWS US- Economic and earnings diary for Jan 4" , it contains no events as it is only contain nouns. So I will embed it as 0 vector in this case.

External answer — Data Science Stack Exchange Author: Nicolas Martin Original post: https://datascience.stackexchange.com/a/111766 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Using triples could lead to wrong results because some headlines could contain double negations or other complex structures that are difficult to classify with triples. However, you can apply directly on the headlines Bert sentiment analysis instead, which can process complex semantics correctly. Here is an example using Bert's twitter roberta sentiment analysis : Note: in this specific case neutral and positive have almost the same value, and you will want to set some threshold to consider a headline as positive, like positive > 0.4. It could also require some fine tuning because tweets are a bit different from headlines. You can even apply sentiment analysis levels (very negative, negative, neutral, positive, very positive) to get even better predictions.