MaltParser - Free Natural Language Processing Software

One of the most commonly used techniques in natural language processing is dependency parsing, which is a specialized analysis process that can provide a way of uncovering the grammatical relationship between words in given sentences. When dealing with treebank data and wanting to integrate it with such processing, users can rely on specialized tools, such as MaltParser.

Key Features:

• Java-based application for data-driven dependency parsing


• Induce parsing models from treebanks


• Parse new data with induced models

This data-driven parser-generator features a different approach compared to traditional ones. While traditional parser-generators rely on constructing parsers using grammar, MaltParser relies on treebanks. Its implementation of inductive dependency parsing involves syntactic analysis of sentences to yield derivations of dependency structures.

Technical Specifications:

• Deterministic parsing algorithms for creating labeled dependency graphs


• History-based models for predicting next parser actions


• Discriminative learning for mapping histories to parser actions

In the process, machine learning is used to guide the parser at nondeterministic choice points. MaltParser's main working principle focuses on these three components to provide efficient dependency parsing.

Developed by Johan Hall, Jens Nilsson, and Joakim Ni, MaltParser is a valuable tool for professionals in the natural language processing field. Enhance your data-driven dependency parsing tasks with MaltParser today for free.