A named entity in Datashare is the name of an individual, an organization or a location.

Datashare’s Named Entity Recognition (NER) uses pipelines of Natural Language Processing (NLP), a branch of artificial intelligence, to automatically highlight named entities in your documents.

As a search operator

In the main search bar, you can write a query with the search operator tilde (~) with a number, at the end of each word of your query. You can set fuzziness to 1 or 2. It corresponds to the maximum number of operations (insertions, deletions, substitutions and transpositions) on characters needed to make one term match the other.

kitten -> sitten (1 substitution (k turned into s) = fuzziness is 1)

kitten -> sittin (2 substitutions (k turned into s and e turned into i) = fuzziness is 2)

If you search for similar terms (to catch typos for example), use fuzziness. Use the tilde symbol at the end of the word to set the fuzziness to 1 or 2.

"The default edit distance is 2, but an edit distance of 1 should be sufficient to catch 80% of all human misspellings. It can be specified as: quikc~1" (source: Elastic).

Example: quikc~ brwn~ foks~ (as the default edit distance is 2, this query will catch all quick, quack, quock, uqikc, etc. as well as brown, folks, etc.)

Example: Datashare~1 (this query will catch Datasahre, Dqtashare, etc.)

In Batch searches

When you run a batch search, you can set the fuzziness to 0, 1 or 2. It is the same as explained above, it will apply to each word in a query and corresponds to the maximum number of operations (insertions, deletions, substitutions and transpositions) on characters needed to make one term match the other.

kitten -> sitten (1 substitution (k turned into s) = fuzziness is 1)

kitten -> sittin (2 substitutions (k turned into s and e turned into i) = fuzziness is 2)

If you search for similar terms (to catch typos for example), use fuzziness. Use the tilde symbol at the end of the word to set the fuzziness to 1 or 2.

"The default edit distance is 2, but an edit distance of 1 should be sufficient to catch 80% of all human misspellings. It can be specified as: quikc~1" (source: Elastic).

Example: quikc~ brwn~ foks~ (as the default edit distance is 2, this query will catch all quick, quack, quock, uqikc, etc. as well as brown, folks, etc.)

Example: Datashare~1 (this query will catch Datasahre, Dqtashare, etc.)

As a search operator

In the main search bar, you can write an exact query in double quotes with the search operator tilde (~) with a number, at the end of your query. It corresponds to the maximum number of operations (insertions, deletions, substitutions and transpositions) on terms needed to make one phrase match the other.

Examples:

“the cat is blue” -> “the small cat is blue” (1 insertion = fuzziness is 1)

“the cat is blue” -> “the small is cat blue” (1 insertion + 2 transpositions = fuzziness is 3)

"While a phrase query (eg "john smith") expects all of the terms in exactly the same order, a proximity query allows the specified words to be further apart or in a different order. A proximity search allows us to specify a maximum edit distance of words in a phrase." (source: Elastic).

Example: "fox quick"~5 (this query will catch "quick brown fox", "quick brown car thin fox" or even "quick brown car thin blue tree fox"

The closer the text in a field is to the original order specified in the query string, the more relevant that document is considered to be. When compared to the above example query, the phrase "quick fox" would be considered more relevant than "quick brown fox"(source: Elastic).

In Batch Searches

When you turn on 'Do phrase matches', you can set, in 'Proximity searches', the maximum number of operations (insertions, deletions, substitutions and transpositions) on terms needed to make one phrase match the other.

“the cat is blue” -> “the small cat is blue” (1 insertion = fuzziness is 1)

“the cat is blue” -> “the small is cat blue” (1 insertion + 2 transpositions = fuzziness is 3)

Example: "fox quick"~5 (this query will catch "quick brown fox", "quick brown car thin fox" or even "quick brown car thin blue tree fox"

Pipelines of Natural Language Processing are tools that automatically identify named entities in your documents. You can only choose one at a time.

Select 'CoreNLP' if you want to use the one with the highest probability of working in most of your documents: