Boundary-based MWE segmentation with text partitioning
Table 1: Evaluation results, including data sets (Experiment); the LFD’s application (LFD); token (qtok) and POS (qPOS) thresholds; precision (P), recall (R), and F-measure (F1); shared-task rank (Rank); and shared task F1 ranges (F1-Range). DIMSUM experiments spanned three domains: Twitter (Tweets), business reviews (Reviews), and TED talk transcripts (TED), with combined evaluation under EN. PARSEME language experiments are identified by ISO 639-1 two-letter codes.
['[BOLD] Experiment', '[BOLD] LFD', '[ITALIC] qtok', '[ITALIC] qPOS', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1', '[BOLD] Rank', '[ITALIC] F1 [BOLD] -range']
Evaluations spanning the variety of languages (19, in total) showed high levels of performance, especially in application to English, where there was a diversity of domains (business reviews, Tweets, and TED talk transcripts), along with comprehensive MWE annotations. Moreover, these results were generally observed for text partitioning both with, and without the LFD. As expected, application of the LFD generally led to increased precision. While integration of POS tags was found to generally improve MWE segmentation in all English experiments, this was frequently not the case in applications to other languages. However, this observation should be taken with consideration for the restriction to the fewer MWE classes (verbal MWEs, only) annotated in the PARSEME (non-English) shared task languages, and additionally the fact that no external data were used. Detailed results for all DIMSUM and PARSEME experiments are recorded in Tab.