Jargon
Jargon is a text pipeline, focused on recognizing variations on canonical and synonymous terms.
For example, jargon lemmatizes react
, React.js
, React JS
and REACTJS
to a canonical reactjs
.
Install
Binaries are available on the Releases page.
If you have Homebrew:
brew install clipperhouse/tap/jargon
If you have a Go installation:
go install github.com/clipperhouse/jargon/cmd/jargon
To display usage, simply type:
jargon
Example:
curl -s https://en.wikipedia.org/wiki/Computer_programming | jargon -html -stack -lemmas -lines
In your code
See GoDoc. Example:
import (
"fmt"
"log"
"strings"
"github.com/clipperhouse/jargon"
"github.com/clipperhouse/jargon/filters/stackoverflow"
)
text := `Let’s talk about Ruby on Rails and ASPNET MVC.`
stream := jargon.TokenizeString(text).Filter(stackoverflow.Tags)
// Loop while Scan() returns true. Scan() will return false on error or end of tokens.
for stream.Scan() {
token := stream.Token()
// Do stuff with token
fmt.Print(token)
}
if err := stream.Err(); err != nil {
// Because the source is I/O, errors are possible
log.Fatal(err)
}
// As an iterator, a token stream is 'forward-only'; once you consume a token, you can't go back.
// See also the convenience methods String, ToSlice, WriteTo
Token filters
Canonical terms (lemmas) are looked up in token filters. Several are available:
Stack Overflow technology tags
Ruby on Rails → ruby-on-rails
ObjC → objective-c
Couldn’t → Could not
café → cafe
Manager|management|manages → manag
To implement your own, see the Filter type.
Performance
jargon
is designed to work in constant memory, regardless of input size. It buffers input and streams tokens.
Execution time is designed to O(n) on input size. It is I/O-bound. In your code, you control I/O and performance implications by the Reader
you pass to Tokenize.
Tokenizer
Jargon includes a tokenizer based partially on Unicode text segmentation. It’s good for many common cases.
It preserves all tokens verbatim, including whitespace and punctuation, so the original text can be reconstructed with fidelity (“round tripped”).
Background
When dealing with technical terms in text – say, a job listing or a resume – it’s easy to use different words for the same thing. This is acute for things like “react” where it’s not obvious what the canonical term is. Is it React or reactjs or react.js?
This presents a problem when searching for such terms. We know the above terms are synonymous but databases don’t.
A further problem is that some n-grams should be understood as a single term. We know that “Objective C” represents one technology, but databases naively see two words.
What’s it for?
- Recognition of domain terms in text
- NLP for unstructured data, when we wish to ensure consistency of vocabulary, for statistical analysis.
- Search applications, where searches for “Ruby on Rails” are understood as an entity, instead of three unrelated words, or to ensure that “React” and “reactjs” and “react.js” and handled synonmously.