Return per-field similarity for each of many fields

Elastic Stack Elasticsearch
1

Context:
We've implemented a learning-to-rank re-scorer for Elasticsearch hits, but (unfortunately) it has to live outside of Elasticsearch, i.e. as a microservice that takes candidates from Elasticsearch, re-scores each hit with a statistical model, and re-ranks those candidates by the model's scores before returning them to the user.

As features, the model uses fields from the document hit, from the query, and the _score, which sort of captures the combination of these.

Goal:
I'd like to extend the statistical model to include the similarity between the query and each of a handful of individual document fields, as separate features. For example, I'd like Elasticsearch to include in the results of a /_search, some calculated fields such as all of these:

  • _score (I use this already, but it's pretty coarse resolution)
  • BM25 between query and document title
  • BM25 between query and document url
  • BM25 between query and document tags

and so on. I'm not picky, if I can't get BM25 exactly, some approximate value that roughly correlates is fine.

Ideally I need to get Elasticsearch to calculate these values and simply return them to me in the response payload. I don't need it to change how Elasticsearch internally computes the _score.

What I've tried

I know that ?explain=true interleaves these calculations directly into my hits, which in theory is all I need. The downside is that it bloats the payload at least 50x (it's sending far more than the few pieces of data I do need), and also it's a pain to programmatically parse out what I need from the explain report.

I think more useful would be to specify script_fields, one for each of the per-field BM25s I want back. Pseudocode:

GET /_search
{
    "query" : {
        "query_string": {
            "query": "purple island",
            "fields": ["title", "url", "description", "tags"]
        }
    },
    "script_fields" : {
        "sim_to_title" : {
            "script" : {
                "source": "doc['title'].PerFieldSimilarity"
            }
        },
        "sim_to_url" : {
            "script" : {
                "source": "doc['url'].PerFieldSimilarity"
            }
        }
    }
}

but I'm at a loss as to what these scripts actually need to look like. I found https://www.elastic.co/guide/en/elasticsearch/painless/6.5/painless-similarity-context.html but not fully sure how to use it; I get the feeling these only work at index time to define a custom similarity metric, whereas I need to return all these values at search time.

What ideas does this community have?

Thanks very much! --Jeff

1 Like
2

There is no way to produce scores through script_fields. Painless has different contexts and in the field context we can only have access to the values of document's fields.

Moreover, each search request will produce scores in a single way. The only way to achieve what you are trying to do is to issue 4 different search requests, 3 of which will be multi match queries.

We also currently working on improving scoring across multiple fields which can also take document statistics into account, but this is work in progress.

3

Thanks for this information! In particular it was helpful to know that script_fields happens in a Painless Field context; I'd seen those contexts listed but the documentation is missing the mapping between context and other ES syntax such as script_fields.

Regarding the "improving scoring across multiple fields" issue... I don't think adds much to my use case, at least in the sense that our machine learning model re-ranks results according to specific combinations of fields, a method that goes beyond the heuristics of BM25/BM25F.

1 Like
4

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