Comparing Sense Categorization Between English PropBank and English WordNet

What it is about

The authors hand-tag verb senses in Penn Treebank sentences with WordNet senses, and compare the result with the senses already given in English PropBank. The sample is 7,576 sentences containing 1,554 senses of 1,453 verbs. For most PropBank senses (1,184 senses of 1,118 verbs) there is a one-to-one match in WordNet. In 352 senses of 329 verbs, one PropBank sense splits into up to 12 WordNet senses, often because of a syntactic difference such as transitive and intransitive use. In a smaller number of cases, several PropBank senses merge into one WordNet sense (6 verbs) or overlap (29 verbs). The authors also compare their manual tags with an automatically built resource: they found 418 matching senses, but 1,721 senses with differences, and they conclude that manual tagging still catches systematic errors that automatic alignment misses.

Ideas

• I1. world/src/dictionary.rs: make a cross-table between Mova's dictionary senses and WordNet synsets for the verbs in the school lessons. Count one-to-one, one-to-many and many-to-one matches as the paper does. The cells with many-to-one matches are the places where the lexicon needs a new sense.
• I2. Add the collocations the paper annotates (for example get up, buy back) as multi-word entries in en/src/dict.rs, and check whether the sense-level accuracy on the school tests rises. The paper finds collocation senses for 18 senses of 15 verbs.
• I3. Use two independent passes by two authors on a sample of verbs, and report agreement before any discussion. Disagreements become the list of senses to revise, not errors to fix silently.

For Mova

The chapter is 19. The lexicon: vocabulary, multiword expressions and register, with 13. Verbs for verb senses. The code is world/src/dictionary.rs (word senses), en/src/dict.rs and en/src/tok.rs (multi-word matching). The measures: the one-to-one share on the lesson verbs, the sense accuracy on the school tests, and the agreement between two passes. The paper's data are not Mova's; its method of cross-checking two sense inventories is the part to use.

Topics: Semantic role labeling

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2019.gwc-1.39

@inproceedings{bakay2019comparing,
  title = {{Comparing Sense Categorization Between English PropBank and English WordNet}},
  author = {Bakay, Özge and Avar, Begüm and Yıldız, Olcay Taner},
  booktitle = {Proceedings of the 10th Global Wordnet Conference},
  year = {2019},
  doi = {10.18653/v1/2019.gwc-1.39},
  url = {https://doi.org/10.18653/v1/2019.gwc-1.39}
}

References

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