(a) Hypernym extraction: for each newly-acquired term/ gloss pair (75, g) E G k, we automatically extract a candidate hypernym h from the textual gloss 9.

To do this we use a simple unsupervised heuristic which just selects the first term in the gloss.5 We show an example of hypernym extraction for some terms in Table 2 (we report the term in column 1, the gloss in column 2 and the hypemyms extracted by the first term hypernym extraction heuristic in column 3).

(b) (Term, Hypernym )-ranking: we sort all the glosses in Gk by the number of seed terms found in each gloss.

Given a domain and a language of interest, we bootstrap the glossary learning process with just a few hypernymy relations (such as computer isa device), with the only condition that the (term, hypernym ) pairs must be specific enough to implicitly identify the domain in the target language.

To avoid the use of a large domain corpus, terminologies can be obtained from the Web by using Doubly-Anchored Patterns (DAPs) which, given a (term, hypernym) pair, harvest sentences matching manually-defined patterns like “< hypernym > such as <term>, and *” (Kozareva et al., 2008).

Similarly to our approach, they drop the requirement of a domain corpus and start from a small number of (term, hypernym ) seeds.

In contrast, GlossBoot performs the novel task of multilingual glossary learning from the Web by bootstrapping the extraction process with a few (term, hypernym ) seeds.

Now, an obvious question arises: what if we bootstrapped GlossBoot with fewer hypernym seeds, e.g., just one seed?

To answer this question we replicated our English experiments on each single (term, hypernym ) pair in our seed set.

we extract the hypernym from the textual definition of p by applying Word-Class Lattices (Navigli and Velardi, 2010, WCL6), a domain-independent hypernym extraction system successfully applied to taxonomy learning from scratch (Velardi et al., 2013) and freely available online (Faralli and Navigli, 2013).

If a hypernym h is successfully extracted and h is linked to a Wikipedia page 19’ for which ,u(p’) is defined, then we extend the mapping by setting Mp) :2 ,u(p’ For instance, the mapping provided by BabelNet does not provide any link for the page Peter Spence; thanks to WCL, though, we are able to set the page Journalist as its hypernym , and link it to the WordNet synsetjournalistk.

The semantic class for a WordNet synset S is the class 0 among those in C which is the most specific hypernym of 8 according to the WordNet taxonomy.

For the joint model, we employed the best single PPR configuration, and a COS configuration that uses sense gloss extended by Wiktionary hypernyms , synonyms and FrameNet frame name and frame definition, to achieve the highest score, an F1-score of 0.739.

We also extract other related lemma-POS, for instance 487 antonyms, 126 hyponyms, and 19 hypernyms .

Relation per FrameNet sense per frame SYNONYM 17,713 13,288 HYPONYM 4,818 3,347 HYPERNYM 6,369 3,961 ANTONYM 9,626 6,737

0 WordNet link types (link type list) (e.g., attribute, hypernym , entailment)

° WordNet hypernyms

Curiously, although hypernyms are commonly used as features in NLP classification tasks, gloss terms, which are rarely used for these tasks, are approximately as useful, at least in this particular context.