Package lingua accurately detects the natural language of written text, be it long or short.
Its task is simple: It tells you which language some provided textual data is written in.
This is very useful as a preprocessing step for linguistic data in natural language
processing applications such as text classification and spell checking.
Other use cases, for instance, might include routing e-mails to the right geographically
located customer service department, based on the e-mails' languages.
Language detection is often done as part of large machine learning frameworks or natural
language processing applications. In cases where you don't need the full-fledged
functionality of those systems or don't want to learn the ropes of those,
a small flexible library comes in handy.
So far, the only other comprehensive open source library in the Go ecosystem for this task
is Whatlanggo (https://github.com/abadojack/whatlanggo).
Unfortunately, it has two major drawbacks:
1. Detection only works with quite lengthy text fragments. For very short text snippets
such as Twitter messages, it does not provide adequate results.
2. The more languages take part in the decision process, the less accurate are the
Lingua aims at eliminating these problems. It nearly does not need any configuration and
yields pretty accurate results on both long and short text, even on single words and phrases.
It draws on both rule-based and statistical methods but does not use any dictionaries of words.
It does not need a connection to any external API or service either.
Once the library has been downloaded, it can be used completely offline.
Compared to other language detection libraries, Lingua's focus is on quality over quantity,
that is, getting detection right for a small set of languages first before adding new ones.
Currently, 75 languages are supported. They are listed as variants of type Language.
Lingua is able to report accuracy statistics for some bundled test data available for each
supported language. The test data for each language is split into three parts:
1. a list of single words with a minimum length of 5 characters
2. a list of word pairs with a minimum length of 10 characters
3. a list of complete grammatical sentences of various lengths
Both the language models and the test data have been created from separate documents of the
Wortschatz corpora (https://wortschatz.uni-leipzig.de) offered by Leipzig University, Germany.
Data crawled from various news websites have been used for training, each corpus comprising one
million sentences. For testing, corpora made of arbitrarily chosen websites have been used,
each comprising ten thousand sentences. From each test corpus, a random unsorted subset of
1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively.
Given the generated test data, I have compared the detection results of Lingua, and
Whatlanggo running over the data of Lingua's supported 75 languages.
Additionally, I have added Google's CLD3 (https://github.com/google/cld3/) to the comparison
with the help of the gocld3 bindings (https://github.com/jmhodges/gocld3). Languages that are not
supported by CLD3 or Whatlanggo are simply ignored during the detection process.
The bar and box plots (https://github.com/pemistahl/lingua-go/blob/main/ACCURACY_PLOTS.md)
show the measured accuracy values for all three performed tasks: Single word detection,
word pair detection and sentence detection. Lingua clearly outperforms its contenders.
Detailed statistics including mean, median and standard deviation values for each language
and classifier are available in
tabular form (https://github.com/pemistahl/lingua-go/blob/main/ACCURACY_TABLE.md) as well.
Every language detector uses a probabilistic n-gram (https://en.wikipedia.org/wiki/N-gram)
model trained on the character distribution in some training corpus. Most libraries only use
n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text
fragments consisting of multiple sentences. For short phrases or single words, however,
trigrams are not enough. The shorter the input text is, the less n-grams are available.
The probabilities estimated from such few n-grams are not reliable. This is why Lingua makes
use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct
A second important difference is that Lingua does not only use such a statistical model, but
also a rule-based engine. This engine first determines the alphabet of the input text and
searches for characters which are unique in one or more languages. If exactly one language can
be reliably chosen this way, the statistical model is not necessary anymore. In any case, the
rule-based engine filters out languages that do not satisfy the conditions of the input text.
Only then, in a second step, the probabilistic n-gram model is taken into consideration.
This makes sense because loading less language models means less memory consumption and better
In general, it is always a good idea to restrict the set of languages to be considered in the
classification process using the respective api methods. If you know beforehand that certain
languages are never to occur in an input text, do not let those take part in the classifcation
process. The filtering mechanism of the rule-based engine is quite good, however, filtering
based on your own knowledge of the input text is always preferable.
There might be classification tasks where you know beforehand that your language
data is definitely not written in Latin, for instance. The detection accuracy
can become better in such cases if you exclude certain languages from the
decision process or just explicitly include relevant languages.
Knowing about the most likely language is nice but how reliable is the
computed likelihood? And how less likely are the other examined languages in
comparison to the most likely one? In the example below, a slice of
ConfidenceValue is returned containing all possible languages sorted by their
confidence value in descending order. The values that this method computes are
part of a relative confidence metric, not of an absolute one. Each value is a
number between 0.0 and 1.0. The most likely language is always returned with
value 1.0. All other languages get values assigned which are lower than 1.0,
denoting how less likely those languages are in comparison to the most likely
The slice returned by this method does not necessarily contain all
languages which the calling instance of LanguageDetector was built from.
If the rule-based engine decides that a specific language is truly
impossible, then it will not be part of the returned slice. Likewise,
if no ngram probabilities can be found within the detector's languages
for the given input text, the returned slice will be empty.
The confidence value for each language not being part of the returned
slice is assumed to be 0.0.
By default, Lingua uses lazy-loading to load only those language models on
demand which are considered relevant by the rule-based filter engine. For web
services, for instance, it is rather beneficial to preload all language models
into memory to avoid unexpected latency while waiting for the service response.
If you want to enable the eager-loading mode, you can do it as seen below.
Multiple instances of LanguageDetector share the same language models in
memory which are accessed asynchronously by the instances.
By default, Lingua returns the most likely language for a given input text.
However, there are certain words that are spelled the same in more than one
language. The word `prologue`, for instance, is both a valid English and
French word. Lingua would output either English or French which might be
wrong in the given context. For cases like that, it is possible to specify a
minimum relative distance that the logarithmized and summed up probabilities
for each possible language have to satisfy. It can be stated as seen below.
Be aware that the distance between the language probabilities is dependent on
the length of the input text. The longer the input text, the larger the
distance between the languages. So if you want to classify very short text
phrases, do not set the minimum relative distance too high. Otherwise Unknown
will be returned most of the time as in the example below. This is the return
value for cases where language detection is not reliably possible.