2023:Word Match (Value Extractor): Difference between revisions
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== Additional Information == | |||
Now that you know how to set up the features that can assist your Word Match extractor, let's look at some additional information regarding some of these features that will help you build a better Word Match extractor. | |||
=== N-Grams: Uni vs Multi === | |||
When it comes to using N-Grams, which is the best approach? Unigrams that will only extract one word, or N-Grams that will pick up multi-word phrases? The ultimate answer is, that it depends on what type of document you're trying to extract data from (and potentially classify), let's look at some examples where a unigram may work over a multi-n-gram and vice versa. | |||
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== See Also: == | == See Also: == | ||
* [[Value Reader]] | * [[Value Reader]] | ||
* [[Data Context]] | * [[Data Context]] | ||
Revision as of 09:17, 7 March 2024
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This article is about an older version of Grooper. Information may be out of date and UI elements may have changed. | |
| 2025 | 2023 |
The Word Match is an Extractor Type found in Grooper. This extractor is designed to collect full words and is often used in n-gram extraction.
About
The Word Match extractor is designed for n-gram extraction. An n-gram is "a contiguous sequence of n items from a given sample of text or speech." [1] Typically in Grooper, this refers to extracting words or phrases from a lexicon of terms.
Grooper generally uses n-grams for the purpose of feature collection for Lexical Classification. The Word Match extractor can capture 1-grams (single words) up to 5-grams (five word phrases). Lexicons are commonly used to dictate a dictionary of allowable returned words. This could be general Lexicon of common English words or a custom Lexicon, such as one with industry specific terms.
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FYI |
An n-gram is often referred to by a different name depending its n size.
As an additional FYI, four-grams are not called "tetragrams" because the term already has usage as a single word consisting of four letters or characters. "Quadrigram" is occasionally used, but four-gram is the more common terminology. Five-grams are not called "pentagrams", because that already has common usage for a geometric figure. |
One area where Word Match sees the most use is in Feature Extractors.
How To
Setup
First, let's set the Word Match extractor on a Data Type.
Adding a Lexicon
You can add a Lexicon to a Word Match to aid in extraction. Word Match often collects "words" that aren't actually in the English language. Let's say you want to change to to only collect words that are included in the Engish dictionary.
Changing the N-Gram
By default the Word Match extractor collects single words or unigrams. Let's say you want to change this to bigrams, trigrams, four-grams, etc. We can do this from the "Properties" tab on the extractor window.
Unigrams vs Multi-Grams
When it comes to using N-Grams, which is the best approach? Unigrams that will only extract one word, or N-Grams that will pick up multi-word phrases? The ultimate answer is, that it depends on what type of document you're trying to extract data from (and potentially classify), let's look at some examples where a unigram may work over a multi-n-gram and vice versa.
Join Patterns
Sometimes words can be separated by something other than a single space. Words can be hyphenated, have commas between them, be connected with an ampersand, or other number of things. To still include these words in an n-gram, you might want to write a Join Pattern using regex for your extractor.
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For information on the Prefix and Suffix patterns, visit the Data Context wiki page. |
Additional Information
Now that you know how to set up the features that can assist your Word Match extractor, let's look at some additional information regarding some of these features that will help you build a better Word Match extractor.
N-Grams: Uni vs Multi
When it comes to using N-Grams, which is the best approach? Unigrams that will only extract one word, or N-Grams that will pick up multi-word phrases? The ultimate answer is, that it depends on what type of document you're trying to extract data from (and potentially classify), let's look at some examples where a unigram may work over a multi-n-gram and vice versa.