[HN Gopher] Relational Graph Convolutional Networks for Sentimen...
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       Relational Graph Convolutional Networks for Sentiment Analysis
        
       Author : PaulHoule
       Score  : 64 points
       Date   : 2024-04-26 22:19 UTC (1 days ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | lmeyerov wrote:
       | Combining BERT + RGCNs is great. Transformers on text +
       | categorical features, then GNNs for learning over connected data,
       | esp their choice of RGCNs for heterogeneous ones.
       | 
       | Some of my favorite use cases to do here are in entity resolution
       | / data cleaning during document ingest, mining social media
       | interactions, and analyzing financial data (loan risk, ...). They
       | all used to largely follow the flow here, just add feature
       | engineering and classification decisions specific to the problem
       | at hand. Especially for scale & automation scenarios where
       | quality matters, this stuff helps.
       | 
       | How I think about this space has changed significantly with
       | modern transformers compared to the BERT-era ones here. The paper
       | feels closer to what we (and others) were doing before GPT4 came
       | out. Now that LLMs can 'reason', not just embed, a lot more has
       | opened up during the feature extraction, learning, and deciding
       | phases. Basically pick up any new KG paper using LLMs, there is a
       | lot to keep up with.
       | 
       | Happy to chat if folks are doing fun things here. We are always
       | looking for good projects in this space as there is nuance and
       | esp with LLMs changing so much. Exciting times!
        
       | adipginting wrote:
       | I came back to to this post several times today to see the
       | comments on this paper. I was curious what is significant about
       | this paper given that it stays on Hacker News front page for
       | hours.
       | 
       | Another curiosity is, what is the typical cost and GPU hours to
       | train the model with these algorithms?
        
         | PaulHoule wrote:
         | This extraction of graph structure is the "holy grail" of NLP
         | in that it can break documents down into facts so that, say,
         | you can store them in a database and query them in a more
         | accurate and efficient way.
         | 
         | Also these science papers frequently have a very low comments
         | to vote ratio compared to, say, articles about cars or the
         | housing supply in California.
        
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       (page generated 2024-04-27 23:02 UTC)