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We examine how differences in language models, learned by different data-driven systems performing the same NLP task, can be exploited to yield a higher accuracy than the best individual system. We do this by means of experiments involving the task of morphosyntactic word class tagging, on the basis of three different tagged corpora. Four well-known tagger generators (hidden Markov model, memory-based, transformation rules, and maximum entropy) are trained on the same corpus data. After comparison, their outputs are combined using several voting strategies and second-stage classifiers. All combination taggers outperform their best component. The reduction in error rate varies with the material in question, but can be as high as 24.3% with the LOB corpus.

Earlier research has shown that established authors can be distinguished by measuring specific properties of their writings, their stylome as it were. Here, we examine writings of less experienced authors. We succeed in distinguishing between these authors with a very high probability, which implies that a stylome exists even in the general population. However, the number of traits needed for so successful a distinction is an order of magnitude larger than assumed so far. Furthermore, traits referring to syntactic patterns prove less distinctive than traits referring to vocabulary, but much more distinctive than expected on the basis of current generativist theories of language learning.

A new technique is introduced, linguistic profiling, in which large numbers of counts of linguistic features are used as a text profile, which can then be compared to average profiles for groups of texts. The technique proves to be quite effective for authorship verification and recognition. The best parameter settings yield a False Accept Rate of 8.1% at a False Reject Rate equal to zero for the verification task on a test corpus of student essays, and a 99.4% 2-way recognition accuracy on the same corpus.

In this paper we examine how the differences in modelling between different data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best individual system. We do this by means of an experiment involving the task of morpho-syntactic wordclass tagging. Four well-known tagger generators (Hidden Markov Model, Memory-Based, Transformation Rules and Maximum Entropy) are trained on the same corpus data. After comparison, their outputs are combined using several voting strategies and second stage classifiers. All combination taggers outperform their best component, with the best combination showing a 19.1% lower error rate than the best individual tagger.