| Tanl Linguistic Pipeline |
Classes | |
| struct | Tanl::TokenField |
| Describes the fields in each token in a corpus. More... | |
| struct | Tanl::CorpusFormat |
| Representatation of Corpus data format. More... | |
| class | Tanl::CorpusFormatError |
| class | Tanl::Corpus |
| Represents common aspects of a Corpus. More... | |
| struct | Tanl::CorpusMap |
| Associates a Corpus constructor to a corpus name. More... | |
| struct | Tanl::Language |
| Functional interface for extractors of morphological features. More... | |
| struct | Tanl::ArabicLanguage |
| struct | Tanl::IndianLanguage |
| struct | Tanl::BanglaLanguage |
| struct | Tanl::BasqueLanguage |
| struct | Tanl::BulgarianLanguage |
| struct | Tanl::CatalanLanguage |
| struct | Tanl::ChineseLanguage |
| struct | Tanl::CzechLanguage |
| struct | Tanl::DanishLanguage |
| struct | Tanl::DutchLanguage |
| struct | Tanl::EnglishLanguage |
| struct | Tanl::FrenchLanguage |
| struct | Tanl::GermanLanguage |
| struct | Tanl::GreekLanguage |
| struct | Tanl::ItalianLanguage |
| struct | Tanl::ItalianTutLanguage |
| struct | Tanl::JapaneseLanguage |
| struct | Tanl::HindiLanguage |
| struct | Tanl::HungarianLanguage |
| struct | Tanl::PortugueseLanguage |
| struct | Tanl::SloveneLanguage |
| struct | Tanl::SpanishLanguage |
| struct | Tanl::SwedishLanguage |
| struct | Tanl::TeluguLanguage |
| struct | Tanl::TurkishLanguage |
| class | Tanl::SentenceReader |
| struct | Tanl::AttributeIndex |
| Associates to an attribute name the index of its value in a attribute vector. More... | |
| struct | Tanl::Attributes |
| Represents the attributes of a token. More... | |
| struct | Tanl::TokenLink |
| Represents links to other tokens. More... | |
| struct | Tanl::Tag |
| Represents SGML tags. More... | |
| struct | Tanl::Context |
| Represents contexts of the tokens. More... | |
| struct | Tanl::Token |
| Represents a sentence token (a word in a sentence). More... | |
| class | Tanl::Classifier::Classifier |
| Abstract class for ML classifiers. More... | |
| class | Tanl::Classifier::Features |
| Representation of features during training. More... | |
| class | Tanl::Classifier::Context |
| Representation of a set of features by means of their internal IDs. More... | |
| class | Tanl::Classifier::FeatureEncoder |
Used to fill Context from unencoded features. More... | |
| class | Tanl::Classifier::GenericEvent< FeatureType > |
| Generic class for events. More... | |
| class | Tanl::Classifier::EventStream |
| Interface for objects that deliver a stream of training events for computing a Maxent model, through GIS, LMBFG or other procedures. More... | |
| class | Tanl::Classifier::FileError |
| class | Tanl::Classifier::EventStreamError |
| class | ME::EventStream |
| A stream of training events. More... | |
| class | Tanl::Classifier::FeatureExtractor< Features, Item > |
| Interface for feature extractors. More... | |
| class | Tanl::Classifier::GIS |
| Generalized Iterative Scaling algorithm to find the parameters {lambda1, . More... | |
| class | Tanl::Classifier::LBFGS |
| class | Tanl::Classifier::MaxEnt |
| A Maximum Entropy classifier. More... | |
| class | Tanl::Classifier::StreamTokenizer |
| class | Tanl::Classifier::FileTokenizer |
| class | Tanl::Enumerator< T > |
| Enumerator interface. More... | |
| struct | Tanl::IPipe< Tin, Tout > |
| IPipe interface. More... | |
| class | Tanl::Iterator< T > |
| Iterator interface. More... | |
| struct | Tanl::RefCountable |
| struct | Tanl::Tagger |
| Interface for tagging each token in a sentence with supplementary information, such as its part of speech. More... | |
| class | Tanl::Timer |
| Class for timing durations. More... | |
Typedefs | |
| typedef std::vector < std::vector< std::pair < ClassID, int > > > | Tanl::Classifier::ContribTable |
| L-BFGS algorithm to find the parameters {alpha1, . | |
| typedef std::vector<std::vector<std::pair<ClassID, int> > > Tanl::Classifier::ContribTable |
L-BFGS algorithm to find the parameters {alpha1, .
.., alphak} of the unique distribution
p* = argmax H(p) p in P
where
P = { p | Epfj = Ep~fj, j = {1, .. , k}}
S = {(a1, b1), ... , (aN, bN)}, ai in A, bj in B (the trainig set)
p(x)the model p's probability of x
p~(x)observerd probability of x in S
Epfj = SUM p(x) fj(x) x in AxB
Ep~fj = SUM p~(x) fj(x) x in AxB