Both the graph-based (McDonald et al., 2005a; McDonald and Pereira, 2006; Carreras et al., 2006) and the transition-based (Yamada and Matsumoto, 2003; Nivre et al., 2006) parsing algorithms are related to our word-pair classification model.

Similar to the graph-based method, our model is factored on dependency edges, and its decoding procedure also aims to find a maximum spanning tree in a fully connected directed graph.

From this point, our model can be classified into the graph-based category.

Previous graph-based dependency models usually use the index distance of word 7' and word j

In contrast, representation used by the graph-based methods (except for TextRank) is based on the word-based graph representation models described in (Schenker et al., 2004).

Today, graph-based text representations are becoming increasingly popular, due to their ability to enrich the document model with syntactic and semantic relations.

(1997) were among the first to make an attempt at using graph-based ranking methods in single document extractive summarization, generating similarity links between document paragraphs and using degree scores in order to extract the important paragraphs from the text.

Erkan and Radev (2004) and Mihalcea (2005) introduced algorithms for unsupervised extractive summarization that rely on the application of iterative graph-based ranking algorithms, such as PageRank (Erin and Page, 1998) and HITS (Kleinberg, 1999).

For dependency parsing, there are two main types of parsing models (Nivre and McDonald, 2008; Nivre and Kubler, 2006): transition-based (Nivre, 2003; Yamada and Matsumoto, 2003) and graph-based (McDonald et al., 2005; Carreras, 2007).

In this paper, we employ the graph-based MST parsing model proposed by McDonald and Pereira

In the graph-based parsing model, features are represented for all the possible relations on single edges (two words) or adjacent edges (three words).

We present a novel, graph-based approach using SimRank, a well-established vertex similarity algorithm to transfer sentiment information between a source language and a target language graph.

Two examples of such methods are a graph-based approach by Dorow et al.

In this paper, we will employ the graph-based method.