Jargon
Jargon is a lemmatizer, useful for recognizing variations on canonical and synonymous terms.
For example, jargon lemmatizes react, React.js, React JS and REACTJS to a canonical reactjs.
Jargon uses Stack Overflow tags & synonyms, and implements “insensitivity” to spaces, dots and dashes.
Online demo
Command line
go install github.com/clipperhouse/jargon/cmd/jargon
(Assumes a Go installation.)
To display usage, simply type:
jargon
jargon accepts piped UTF8 text from Stdin and pipes lemmatized text to Stdout
Example: echo "I luv Rails", jargon
Alternatively, use jargon 'standalone' by passing flags for inputs and outputs:
-f string
Input file path
-o string
Output file path
-s string
A (quoted) string to lemmatize
-u string
A URL to fetch and lemmatize
Example: jargon -f /path/to/original.txt -o /path/to/lemmatized.txt
In your code
See GoDoc.
Dictionaries
Canonical terms (lemmas) are looked up in dictionaries. Three are available:
- Stack Exchange technology tags
Ruby on Rails → ruby-on-railsObjC → objective-c
- Contractions
Couldn‘t → Could not
- Simple numbers
Thirty-five hundred → 3500
To implement your own, see the jargon.Dictionary interface
Tokenizer
Jargon includes its own tokenizer, with an emphasis on handling technology terms correctly:
- C++, ASP.net, and other non-alphanumeric terms are recognized as single tokens
- #hashtags and @handles
- Simple URLs and email address are handled pretty well, though can be notoriously hard to get right
The tokenizer preserves all tokens verbatim, including whitespace and punctuation, so the original text can be reconstructed with fidelity (“round tripped”).
(It turns out that the above rules work well in structured text such as CSV and JSON.)
Background
When dealing with technology 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.
Prior art
Existing tokenizers (such as Treebank), appear not to be round-trippable, i.e., are destructive. They also take a hard line on punctuation, so “ASP.net” would come out as two tokens instead of one. Of course I’d like to be corrected or pointed to other implementations.
Search-oriented databases like Elastic handle synonyms with analyzers.
In NLP, it’s handled by stemmers or lemmatizers. There, the goal is to replace variations of a term (manager, management, managing) with a single canonical version.
Recognizing mutli-words-as-a-single-term (“Ruby on Rails”) is named-entity recognition.
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.