Experiments and Analysis | McDonald and Pereira (2006) propose a second-order graph-based parser, but use a smaller feature set than our work. |
Experiments and Analysis | K00 and Collins (2010) propose a third-order graph-based parser. |
Introduction | For example, Koo and Collins (2010) and Zhang and McDonald (2012) show that incorporating higher-order features into a graph-based parser only leads to modest increase in parsing accuracy. |
Introduction | To construct parse forest on unlabeled data, we employ three supervised parsers based on different paradigms, including our baseline graph-based dependency parser, a transition-based dependency parser (Zhang and Nivre, 2011), and a generative constituent parser (Petrov and Klein, 2007). |
Supervised Dependency Parsing | The graph-based method views the problem as finding an optimal tree from a fully-connected directed graph (McDonald et al., 2005; McDonald and Pereira, 2006; Carreras, 2007; K00 and Collins, 2010), while the transition-based method tries to find a highest-scoring transition sequence that leads to a legal dependency tree (Yamada and Matsumoto, 2003; Nivre, 2003; Zhang and Nivre, 2011). |
Supervised Dependency Parsing | 2.1 Graph-based Dependency Parser (GParser) |
Supervised Dependency Parsing | In this work, we adopt the graph-based paradigm because it allows us to naturally derive conditional probability of a dependency tree (1 given a sentence X, which is required to compute likelihood of both labeled and unlabeled data. |
Analysis | (Grineva et al., 2009) [X] News Graph-based ranking (DUC for extended neighborhood 28.8 35.4 31.7 -2001) (Wan and Xiao, 2008b) [><] Papers Statistical, semantic, and |
Keyphrase Extraction Approaches | 3.3.1 Graph-Based Ranking |
Keyphrase Extraction Approaches | The basic idea behind a graph-based approach is to build a graph from the input document and rank its nodes according to their importance using a graph-based ranking method (e.g., Erin and Page (1998)). |
Keyphrase Extraction Approaches | TextRank (Mihalcea and Tarau, 2004) is one of the most well-known graph-based approaches to keyphrase extraction. |
Introduction | In order to address these unique challenges for wikification for the short tweets, we employ graph-based semi-supervised learning algorithms (Zhu et al., 2003; Smola and Kondor, 2003; Blum et al., 2004; Zhou et al., 2004; Talukdar and Crammer, 2009) for collective inference by exploiting the manifold (cluster) structure in both unlabeled and labeled data. |
Introduction | effort to explore graph-based semi-supervised learning algorithms for the wikification task. |
Related Work | Non-collective methods usually rely on prior popularity and context similarity with supervised models (Mihalcea and Csomai, 2007; Milne and Witten, 2008b; Han and Sun, 2011), while collective approaches further leverage the global coherence between concepts normally through supervised or graph-based re-ranking models (Cucerzan, 2007; Milne and Witten, 2008b; Han and Zhao, 2009; Kulkarni et al., 2009; Pennacchiotti and Pantel, 2009; Ferragina and Scaiella, 2010; Fernandez et al., 2010; Radford et al., 2010; Cucerzan, 2011; Guo et al., 2011; Han and Sun, 2011; Han et al., 2011; Ratinov et al., 2011; Chen and Ji, 2011; Kozareva et al., 2011; Cassidy et al., 2012; Shen et al., 2013; Liu et al., 2013). |
Related Work | This work is also related to graph-based semi-supervised learning (Zhu et al., 2003; Smola and Kondor, 2003; Zhou et al., 2004; Talukdar and Crammer, 2009), which has been successfully applied in many Natural Language Processing tasks (Niu et al., 2005; Chen et al., 2006). |
Relational Graph Construction | Compared to the referent graph which considers each mention or concept as a node in previous graph-based re-ranking approaches (Han et al., 2011; Shen et al., 2013), our |
Relational Graph Construction | (ii) It is more appropriate for our graph-based semi-supervised model since it is difficult to assign labels to a pair of mention and concept in the referent graph. |
Semi-supervised Graph Regularization | We propose a novel semi-supervised graph regularization framework based on the graph-based semi-supervised learning algorithm (Zhu et al., 2003): |
Abstract | In this work, we present a semi-supervised graph-based approach for generating new translation rules that leverages bilingual and monolingual data. |
Generation & Propagation | Otherwise it is called an unlabeled phrase, and our algorithm finds labels (translations) for these unlabeled phrases, with the help of the graph-based representation. |
Introduction | Our work introduces a new take on the problem using graph-based semi-supervised learning to acquire translation rules and probabilities by leveraging both monolingual and parallel data resources. |
Related Work | Recent improvements to BLI (Tamura et al., 2012; Irvine and Callison-Burch, 2013b) have contained a graph-based flavor by presenting label propagation-based approaches using a seed lexicon, but evaluation is once again done on top-1 or top-3 accuracy, and the focus is on unigrams. |
Related Work | (2013) and Irvine and Callison-Burch (2013a) conduct a more extensive evaluation of their graph-based BLI techniques, where the emphasis and end-to-end BLEU evaluations concentrated on OOVs, i.e., unigrams, and not on enriching the entire translation model. |
Related Work | aged to have similar target language translations, has also been explored via a graph-based approach (Alexandrescu and Kirchhoff, 2009). |
Experiments | DWSA stands for Dijkstra-WSA, the state-of-the-art graph-based alignment approach of Matuschek and Gurevych (2013). |
Introduction | However, not all lexical resources provide explicit semantic relations between concepts and, hence, machine-readable dictionaries like Wiktionary have first to be transformed into semantic graphs before such graph-based approaches can be applied to them. |
Related Work | Last year Matuschek and Gurevych (2013) proposed Dijkstra-WSA, a graph-based approach relying on shortest paths between two concepts when the two corresponding resources graphs were combined by leveraging monosemous linking. |
Resource Alignment | The structural similarity component, instead, is a novel graph-based similarity measurement technique which calculates the similarity between a pair of concepts across the semantic networks of the two resources by leveraging the semantic |
Abstract | We develop graph-based ranking models that automatically select the best output from multiple redundant versions of translations and edits, and improves translation quality closer to professionals. |
Evaluation | Using the raw translations without post-editing, our graph-based ranking method achieves a BLEU score of 38.89, compared to Zaidan and Callison-Burch (2011)’ s reported score of 28.13, which they achieved using a linear feature-based classification. |
Evaluation | In contrast, our proposed graph-based ranking framework achieves a score of 41.43 when using the same information. |
Introduction | 0 A new graph-based algorithm for selecting the best translation among multiple translations of the same input. |