Neural Dependency Parser

As an alternative to rule-based Txala dependency parser and dep_treeler statistical parser, a neural-network based dependency parsing module is also available. It is based on the LSTM parser proposed by [DBL+15].

The API of the class is the following:

class dep_lstm : public dependency_parser {
 public:   
   /// constructor
   dep_lstm(const std::string &cfgfile);
   /// destructor
   ~dep_lstm();

   /// analyze given sentence.
   void analyze(sentence &s) const;

   /// analyze given sentences.
   void analyze(std::list<sentence> &ls) const;

   /// return analyzed copy of given sentence
   sentence analyze(const sentence &s) const;

   /// return analyzed copy of given sentences
   std::list<sentence> analyze(const std::list<sentence> &ls) const;
};

Neural Parser Configuration File

The constructor for class dep_lstm expects a configuration file, which is created by the training scripts. Attempts to customize it will result in a poor performance.