Skip to content

NLP: Indexing and Topics

Today we will talk about how to create an index and how that makes searching easier. If time, we will address how this can help to understand topic models.

Basic tabular lookups

The most basic search would look like this:

key record
cat PET0001
dog PET0002
mouse PET0003 
term = "dog"
for key, record in row:
    if term == key:
        return record

Time for tabular lookups

  • Estimated 500 nanoseconds per record for basic search
  • (500 * 1,000,000) nanoseconds = 0.5 seconds
  • Current Catalogue of Life has 7,000,000 records = 3.5 seconds

Edit distance

Last time we talked about edit distance. A quick reminder:

cat is 1 edit away from cats cut is 1 edit away from cat

Edit distance search

  • 1000 nanoseconds per edit distance check
  • ((1000 + 500) * 7,000,000) nanoseconds = 10.5 seconds
  • 10.5 seconds * 2,000,000 items in the Beaty = 243.055556 days

Can't do this!

Input cleaning (stemming)

One approach is to simply clean the input.

term = "doggies"
lookup = stem(term) # dog
for key, record in row:
    if lookup == key:
        return record

Indexing: rank search

A better approach is to pre-compute a distance metric and build an index.

term synonym distance
kitten kittie 1
kitten cat 2

But how do we create this sense of semantic distance?

Relatedness and search

In the Beaty, we have a few potential distance metrics:

  1. Taxonomic proximity
  2. Vernacular names co-occurence
  3. Other relations (spatial, temporal, topical, etc.)

Tree-based semantic distance

There is a semantic similarity between cats and dogs. If you have a tree like:

pets -> cats pets -> dogs

You can say that cats are related to dogs via one parent, pets. There are big hand-developed dictionaries that relate concepts that can give you a distance.

Taxonomic proximity

  • Allophrynidae Allophryne resplendens is a sibling of Allophrynidae Allophryne ruthveni
  • You could define proximity to be number of steps to a common parent.
  • Therefore, this would be a distance of 1.

Vernacular name co-occurence

The Catalogue of Life gives us a list of vernacular names.

ID Lang Name
74YS2 eng slime membrane sponge
74YS2 fra éponge poisseuse
6SYS3 deu Feigenbohrschwamm
6SYS3 deu Fleischschwamm
6SYS3 spa esponja azul
6SYS3 eng flesh sponge
6SYS3 eng lobate fig sponge

Vernacular name co-occurence

We could define a vernacular distance.

ID Lang Name
74YS2 eng slime membrane sponge
6SYS3 eng lobate fig sponge
ID_1 ID_2 Key Distance
74YS2 6SYS3 sponge 1

Beaty's special co-location metric

Let's define the following metrics:

  • Collection event distance
  • Collector distance
  • Remarks distance

Topic models

Topic models are basically automatically-generated groupings of similar records based on custom metrics.