We study in this section the complexity of several decision problems on MLIGs, prominently of emptiness and membership problems, in the general (Section 4.2), k-bounded (Section 4.3), and lexicalized cases (Section 4.4).

4.4 Lexicalized Case

2. the effects of two linguistically motivated restrictions on such formalisms, lexicalization and boundedness/rankedness.

Two restrictions on dominance links have been suggested in an attempt to reduce their complexity, sometimes in conjunction: lexicalization and k-boundedness.

We can combine the two restrictions, thus defining the class of k:-bounded lexicalized MLIGs.

Lexicalization Lexicalization in UVG-dls reflects the strong dependence between syntactic constructions (vectors of productions representing an extended domain of locality) and lexical anchors.

3 Adding terminal symbols 0 in each production would result in a lexicalized grammar, still with a non semilinear language.

Lexicalization has now its usual definition: for every vector ({pi,1, .

Importantly, each vertex v E V contains a set of lexicalizations of the concept for different languages, e.g.

We call the resulting set of multilingual lexicalizations of a given concept a babel synset.

An overview of BabelNet is given in Figure l (we label vertices with English lexicalizations ): unlabeled edges are obtained from links in the Wikipedia pages (e.g.

However, it does not say anything about the precision of the additional lexicalizations provided by BabelNet.

those mapped with our method illustrated in Section 3.2), 200 synsets whose lexicalizations exist in Wikipedia only.

lexicalizations ) were appropriate given the corresponding WordNet gloss and/or Wikipage.

By repeating this step for each English lexicalization in a babel synset, we obtain a collection of sentences for the babel synset (see left part of Figure 1).

Note that we had no translation for Catalan and French in the first phase, because the inter-language link was not available, and we also obtain new lexicalizations for the Spanish and Italian languages.

Combinatory Categorial Grammar (CCG) is generally construed as a fully lexicalized formalism, where all grammars use one and the same universal set of rules, and cross-linguistic variation is isolated in the lexicon.

This is what makes pure CCG a lexicalized grammar formalism (Steedman and Baldridge, 2010).

This means that these formalisms cannot be fully lexicalized , in the sense that certain languages can only be described by selecting language-specific rules.

This shows that the generative capacity of at least first-order CCG crucially relies on its ability to restrict rule instantiations, and is at odds with the general conception of CCG as a fully lexicalized formalism, in which all grammars use one and the same set of universal rules.

This means that word order in CCG cannot be fully lexicalized with the current formal tools; some ordering constraints must be specified via language-specific combination rules and not in lexicon entries.

We propose a novel self-training method for a parser which uses a lexicalised grammar and supertagger, focusing on increasing the speed of the parser rather than its accuracy.

Lexicalised grammars typically contain a much smaller set of rules than phrase-structure grammars, relying on tags (supertags) that contain a more detailed description of each word’s role in the sentence.

Figure 1 gives two sentences and their CCG derivations, showing how some of the syntactic ambiguity is transferred to the supertagging component in a lexicalised grammar.

Parsing with lexicalised grammar formalisms, such as Lexicalised Tree Adjoining Grammar and Combinatory Categorial Grammar (CCG; Steed-man, 2000), can be made more efficient using a supertagger.

More generally, the parser used in these evaluations differs from other reported parsers in that it is not lexicalized .

However, we see that this language model performs well despite its lack of lexicalization .

This indicates that lexicalization is not a requisite part of syntactic parser performance with respect to predicting linguistic complexity, corroborating the evidence of Demberg and Keller’s (2008) ‘unlexicalized’ (POS-generating, not word-generating) parser.

Head-driven phrase structure grammar (HPSG) is a lexicalist grammar framework.

Based on TC, we can easily build a tree-to-string translation rule by further completing the right-hand-side string by sorting the spans of Tc’s leaf nodes, lexicalizing the terminal node’s span(s), and assigning a variable to each nonterminal node’s span.

Two kinds of supertags, from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar (CCG), have been used as lexical syntactic descriptions (Hassan et al., 2007) for phrase-based SMT (Koehn et al., 2007).

Like TextRunner, WOE’s extractor eschews lexicalized features and handles an unbounded set of semantic relations.

We are also interested in merging lexicalized and open extraction methods; the use of some domain-specific lexical features might help to improve WOE’s practical performance, but the best way to do this is unclear.

First, J iang and Zhai’s results are tested for traditional IE where local lexicalized tokens might contain sufficient information to trigger a correct classification.

Of especial interest are deep lexicalized rules such as

One approach is to use word alignments (where these can be reliably estimated, as in our testbed application) to align subtrees and extract rules (Och and Ney, 2004; Galley et al., 2004) but this leaves open the question of finding the right level of generality of the rules — how deep the rules should be and how much lexicalization they should involve — necessitating resorting to heuristics such as minimality of rules, and leading to

Second, the ability to have rules deeper than one level provides a principled way of modeling lexicalization , whose importance has been emphasized (Galley and McKeown, 2007; Yamangil and Nelken, 2008).