Using Estimation Maximization to induce verb and noun classes based on extracted verb-noun-pairs, following Rooth et al. (1999)
| Variable Name | Content |
|---|---|
| p_c | Probability for each class |
| p_v_c | Probability for a verb being in a class |
| p_n_c | Probability for a noun being in a class |
| pairs_classes | Joint probability for a verb and noun being in a class |
| v_c | Hard verb classes |
| n_c | Hard noun classes |
| verb_noun_rank | Ranked nouns for a intransitive verb |
| verb_noun_value | Accumulated probability for all nouns belonging to an intransitive verb |
- gold_deps.txt: Reduced data
- all_pairs: All pairs, not preprocessed
- new_pairs: All pairs, preprocessed
| Abbr. | Explanation |
|---|---|
| s | intransitive verb |
| so | transitive verb |
| nsubj | nominal subject |
| csubj | clausal subject |
| nsubjpass | nominal subject in passive construction |
| dobj | direct object |
| iobj | indirect object |
| Verb | Co-occurring noun |
|---|---|
| testify_s_nsubj | list |
| make_so_nsubj | teacher |
| lead_s_csubj | deciding |
| give_so_csubj | adding |
| leave_s_nsubjpass | vegetable |
| make_so_nsubjpass | project |
| reach_so_dobj | leader |
| become_so_iobj | member |
Class 5: Change on a Scale - Numbers
| Verbs | Prob | Nouns | Prob |
|---|---|---|---|
| pay_so_dobj | 0.0674 | price | 0.0389 |
| rise_s_nsubj | 0.0623 | number | 0.0304 |
| fell_s_nsubj | 0.0459 | attention | 0.0300 |
| draw_so_dobj | 0.0349 | profit | 0.0257 |
| fall_s_nsubj | 0.0203 | rate | 0.0257 |
| rise_so_nsubj | 0.0193 | share | 0.0226 |
| bring_so_dobj | 0.0191 | cost | 0.0210 |
| increase_so_dobj | 0.0185 | sale | 0.0169 |
| cut_so_dobj | 0.0179 | earnings | 0.0152 |
| increase_s_nsubj | 0.0168 | level | 0.0107 |
begin_s_nsubj
| Noun | Score |
|---|---|
| work | 11.3236 |
| process | 8.4428 |
| which | 6.3095 |
| trial | 6.2411 |
| discussion | 5.9485 |
| phase | 5.8081 |
| career | 5.6166 |
| shipment | 5.5813 |
| series | 5.4629 |
| programme | 5.2699 |
- Achieving tracable computation of transitive verbs with ranked nouns
Mats Rooth, Stefan Riezler, Detlef Prescher, Glenn Carroll, and Franz Beil. 1999. Inducing a semantically annotated lexicon via EM-based clustering. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics. Association for Computational Linguistics, Stroudsburg, PA, USA, ACL ’99, pages 104–111. http://www.aclweb.org/anthology/P99-1014.