Speeding up Natural Language Parsing by Reusing Partial Results
Table 3: Performance of BIST Parser and MaltParser with the arc-eager and Covington transition systems and after applying templates compared with the baseline. The reported parsing speed (tokens/sec) only refers to the runtime of the dependency parser on the entire data set (baseline) or remaining text (that was passed to the parser after extracting the fragments captured by the templates) excluding the time needed to run the technique which is already negligible and will be optimized in the future versions.
['Parser', 'Data Set', 'Setup', 'UAS (%)', 'LAS (%)', 'Word Reduction (%)', 'Tokens/Sec', 'Speed-up Factor']
The results are revealing in several ways. The experiments confirm that the more lenient confidence thresholds result in larger reductions of input length and thus faster parsing, but at the expense of accuracy. This applies to all parsers and indicates that our approach can be generic and applicable to diverse parsing algorithms. Moreover, the results show that it is feasible to reduce input size by more than 20% at the cost of less than 3 points of unlabeled attachment score.