Elasticsearch exact matches on analyzed fields

Question

Is there a way to have ElasticSearch identify exact matches on analyzed fields? Ideally, I would like to lowercase, tokenize, stem and perhaps even phoneticize my docs, then

Answer 1

Use shingles tokenizer together with stemming and whatever else you need. Add a sub-field of type token_count that will count the number of tokens in the field.

At searching time, you need to add an additional filter to match the number of tokens in the index with the number of tokens you have in the searching text. You would need an additional step, when you perform the actual search, that should count the tokens in the searching string. This is like this because shingles will create multiple permutations of tokens and you need to make sure that it matches the size of your searching text.

An attempt for this, just to give you an idea:

{
  "settings": {
    "analysis": {
      "filter": {
        "filter_shingle": {
          "type": "shingle",
          "max_shingle_size": 10,
          "min_shingle_size": 2,
          "output_unigrams": true
        },
        "filter_stemmer": {
          "type": "porter_stem",
          "language": "_english_"
        }
      },
      "analyzer": {
        "ShingleAnalyzer": {
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "snowball",
            "filter_stemmer",
            "filter_shingle"
          ]
        }
      }
    }
  },
  "mappings": {
    "test": {
      "properties": {
        "text": {
          "type": "string",
          "analyzer": "ShingleAnalyzer",
          "fields": {
            "word_count": {
              "type": "token_count",
              "store": "yes",
              "analyzer": "ShingleAnalyzer"
            }
          }
        }
      }
    }
  }
}

And the query:

{
  "query": {
    "filtered": {
      "query": {
        "match_phrase": {
          "text": {
            "query": "HaMbUrGeRs BUN"
          }
        }
      },
      "filter": {
        "term": {
          "text.word_count": "2"
        }
      }
    }
  }
}

The shingles filter is important here because it can create combinations of tokens. And more than that, these are combinations that keep the order or the tokens. Imo, the most difficult requirement to fulfill here is to change the tokens (stemming, lowercasing etc) and, also, to assemble back the original text. Unless you define your own "concatenation" filter I don't think there is any other way than using the shingles filter.

But with shingles there is another issue: it creates combinations that are not needed. For a text like "Hamburgers buns in Los Angeles" you end up with a long list of shingles:

          "angeles",
          "buns",
          "buns in",
          "buns in los",
          "buns in los angeles",
          "hamburgers",
          "hamburgers buns",
          "hamburgers buns in",
          "hamburgers buns in los",
          "hamburgers buns in los angeles",
          "in",
          "in los",
          "in los angeles",
          "los",
          "los angeles"

If you are interested in only those documents that match exactly meaning, the documents above matches only when you search for "hamburgers buns in los angeles" (and doesn't match something like "any hamburgers buns in los angeles") then you need a way to filter that long list of shingles. The way I see it is to use word_count.