An integrated architecture for shallow and deep processing

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis

Topic Map Generation Using Text Mining

Starting from text corpus analysis with linguistic and statistical analysis algorithms, an infrastructure for text mining is described which uses collocation analysis as a central tool. This text mining method may be applied to different domains as well as languages. Some examples taken form large reference databases motivate the applicability to knowledge management using declarative standards of information structuring and description. The ISO/IEC Topic Map standard is introduced as a candidate for rich metadata description of information resources and it is shown how text mining can be used for automatic topic map generation

Preparing, restructuring, and augmenting a French treebank: lexicalised parsers or coherent treebanks?

We present the Modified French Treebank (MFT), a completely revamped French Treebank, derived from the Paris 7 Treebank (P7T), which is cleaner, more coherent, has several transformed structures, and introduces new linguistic analyses. To determine the effect of these changes, we investigate how theMFT fares in statistical parsing. Probabilistic parsers trained on the MFT training set (currently 3800 trees) already perform better than their counterparts trained on five times the P7T data (18,548 trees), providing an extreme example of the importance of data quality over quantity in statistical parsing. Moreover, regression analysis on the learning curve of parsers trained on the MFT lead to the prediction that parsers trained on the full projected 18,548 tree MFT training set will far outscore their counterparts trained on the full P7T. These analyses also show how problematic data can lead to problematic conclusions–in particular, we find that lexicalisation in the probabilistic parsing of French is probably not as crucial as was once thought (Arun and Keller (2005))