Dictionary API

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

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 text 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 detection results. 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. Lingua clearly outperforms its contenders. 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 language. 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 runtime performance. 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 those languages which the calling instance of LanguageDetector has been built from. The entries are sorted by their confidence value in descending order. Each value is a probability between 0.0 and 1.0. The probabilities of all languages will sum to 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned for this language. The other languages will receive a value of 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.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package cedict provides a parser / tokenizer for reading entries from the CEDict Chinese dictionary project. Tokenizing is done by creating a CEDict for an io.Reader r. It is the caller's responsibility to ensure that r provides a CEDict-formatted dictionary. Given a CEDict c, the dictionary is tokenized by repeatedly calling c.NextEntry(), which parses until it reaches the next entry, or an error if no more entries are found: To retrieve the current entry, the Entry method can be called. There is also a lower-level API available, using the bufio.Scanner Scan method. Using this lower-level API is the recommended way to read comments from the CEDict, should that be necessary.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

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 text 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 detection results. 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. Lingua clearly outperforms its contenders. 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 language. 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 runtime performance. 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 those languages which the calling instance of LanguageDetector has been built from. The entries are sorted by their confidence value in descending order. Each value is a probability between 0.0 and 1.0. The probabilities of all languages will sum to 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned for this language. The other languages will receive a value of 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.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

yugur is a lightweight self-hosted dictionary platform.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.

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 detection results. 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 language. 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 runtime performance. 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 language. 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.

Package pdf implements reading of PDF files. PDF is Adobe's Portable Document Format, ubiquitous on the internet. A PDF document is a complex data format built on a fairly simple structure. This package exposes the simple structure along with some wrappers to extract basic information. If more complex information is needed, it is possible to extract that information by interpreting the structure exposed by this package. Specifically, a PDF is a data structure built from Values, each of which has one of the following Kinds: The accessors on Value—Int64, Float64, Bool, Name, and so on—return a view of the data as the given type. When there is no appropriate view, the accessor returns a zero result. For example, the Name accessor returns the empty string if called on a Value v for which v.Kind() != Name. Returning zero values this way, especially from the Dict and Array accessors, which themselves return Values, makes it possible to traverse a PDF quickly without writing any error checking. On the other hand, it means that mistakes can go unreported. The basic structure of the PDF file is exposed as the graph of Values. Most richer data structures in a PDF file are dictionaries with specific interpretations of the name-value pairs. The Font and Page wrappers make the interpretation of a specific Value as the corresponding type easier. They are only helpers, though: they are implemented only in terms of the Value API and could be moved outside the package. Equally important, traversal of other PDF data structures can be implemented in other packages as needed.