This general framework allows us to use arbitrary similarity functions between items, and to incorporate different information in our comparison, such as n-grams, dependency relations , etc.

In this paper, we propose a new automatic MT evaluation metric, MAXSIM, that compares a pair of system-reference sentences by extracting n-grams and dependency relations .

Recognizing that different concepts can be expressed in a variety of ways, we allow matching across synonyms and also compute a score between two matching items (such as between two n-grams or between two dependency relations ), which indicates their degree of similarity with each other.

Also, this framework allows for defining arbitrary similarity functions between two matching items, and we could match arbitrary concepts (such as dependency relations ) gathered from a sentence pair.

Hence, using information such as synonyms or dependency relations could potentially address the issue better.

4.2 Dependency Relations

Hence, besides matching based on n-gram strings, we can also match other “information items”, such as dependency relations .

We propose using large-scale clustering of dependency relations between verbs and multi-word nouns (MN 5) to construct a gazetteer for named entity recognition (N ER).

Since dependency relations capture the semantics of MN 5 well, the MN clusters constructed by using dependency relations should serve as a good gazetteer.

2.2 EM-based Clustering using Dependency Relations

g of Dependency Relations

By paralleliz-ing the clustering algorithm, we successfully constructed a cluster gazetteer with up to 500,000 entries from a large amount of dependency relations in Web documents.

Dependency relation (DR): Our way to overcome data sparsity is to break lexicalized frames into lexicalized slots (a.k.a.

dependency relations ).

Dependency relations contain both syntactic and lexical information (4).

However, they cannot capture the dependency relationship between sentences.

To label 810, we need consider the dependency relation between Q2 and Q3.

The labels of the same sentence for two contiguous questions in a thread would be conditioned on the dependency relationship between the questions.

One is the dependency relationship between contexts and answers, which should be leveraged especially when questions alone do not provide sufficient information to find answers; the other is the dependency between answer candidates (similar to sentence dependency described above).

The edges eXpress dependency relations between nodes.

The predicative complement is a nonobligatory free modification (adjunct) which has a dual semantic dependency relation .

These two dependency relations are represented by different means (t-manual, page 376):