This paper describes the lemmatisation of three annotated corpora of Middle English—the Penn-Helsinki Parsed Corpus of Middle English 2 (PPCME2), the Parsed Corpus of Middle English Poetry (PCMEP), and A Parsed Linguistic Atlas of Early Middle English (PLAEME) — which is a prerequisite for systematically investigating the argument structures of verbs of the given time. Creating this tool and enriching existing parsed corpora of Middle English is part of the project Borrowing of Argument Structure in Contact Situations (BASICS) which seeks to explain to which extent verbs copied from Old French had an impact on the grammar of Middle English. First, we lemmatised the PPCME2 by (1) creating an inventory of form-lemma correspondences linking forms in the PPCME2 to lemmas in the MED, and (2) inserting this lemma information into the corpus (precision: 94.85%, recall: 98.92%). Second, we enriched the PCMEP and PLAEME, which adopted the annotation format of the PPCME2, with verb lemmas to undertake studies that fill the well-known data gap in the subperiod (1250–1350) of the PPCME2. The case study of reflexives shows that with our method we gain much more reliable results in terms of diachrony, diatopy and contact-induced change.

The paper introduces a “train once, use many” approach for the syntactic analysis of phrasal compounds (PC) of the type XP+N like “Would you like to sit on my knee?” nonsense. PCs are a challenge for NLP tools since they require the identification of a syntactic phrase within a morphological complex. We propose a method which uses a state-of-the-art dependency parser not only to analyse sentences (the environment of PCs) but also to compound the non-head of PCs in a well-defined particular condition which is the analysis of the non-head spanning from the left boundary (mostly marked by a determiner) to the nominal head of the PC. This method contains the following steps: (a) the use an English state-of-the-art dependency parser with data comprising sentences with PCs from the British National Corpus (BNC), (b) the detection of parsing errors of PCs, (c) the separate treatment of the non-head structure using the same model, and (d) the attachment of the non-head to the compound head. The evaluation of the method showed that the accuracy of 76% could be improved by adding a step in the PC compounder module which specified user-defined contexts being sensitive to the part of speech of the non-head parts and by using TreeTagger, in line with our approach.