Abstrakt
The Reading Machine, is a parsing framework that takes as input raw text and performs six standard NLP tasks: tokenization, POS tagging, morphological analysis, lemmatization, dependency parsing and sentence segmentation. It is built upon Transition Based Parsing, and allows implementing a large number of parsing configurations, among which a fully incremental one.
Three case studies are presented to highlight the versatility of the framework. The first one explores whether an incremental parser is able to take into account top-down dependencies (i.e. the influence of high level decisions on low level ones), the second compares the performances of an incremental and a pipe-line architecture and the third quantifies the impact of the right context on the predictions made by an incremental parser.