| P06-1101 | for finding T for the case of | hyponym acquisition | . We assume we begin with some |
| P06-1101 | Hyponym Acquisition For the case of | hyponym acquisition | , the objects in our taxonomy |
| P06-1101 | ( i , j ) in T. Relations for | Hyponym Acquisition | For the case of hyponym acquisition |
| P06-1101 | best-first search over taxonomies for | hyponym acquisition | remains un - changed . One side |
| W14-1608 | report the results of a traditional | hyponym acquisition | system . For this , we implemented |
| P06-1101 | its hypernyms . For the case of | hyponym acquisition | we enforce the following two |
| P06-1101 | implied links for the purpose of | hyponym acquisition | in Section 3.4 . 2.2 A Probabilistic |
| P06-1101 | taxonomy induction , we have applied | hyponym acquisition | to construct several distinct |
| W14-1608 | model smoothing . Pattern-based | hyponym acquisition | can be used to find relevant |
| P08-1119 | rubrics of lexical acquisition , | hyponym acquisition | , semantic lexicon induction |
| P06-1101 | problem of sense-disambiguated noun | hyponym acquisition | , where we combine the predictions |
| P06-1101 | Computational Linguistics duction and | hyponym acquisition | : the problem of combining heterogenous |
| W14-1608 | Lenci , 2011 ) . In con - trast , | hyponym acquisition | is the task of extracting all |
| P06-1101 | Details of our Implementation | Hyponym acquisition | is among the simplest and most |
| P06-1101 | best-first search algorithm for | hyponym acquisition | , which at each iteration defines |
