Index of papers in Proc. ACL 2014 that mention
  • graph-based
Li, Zhenghua and Zhang, Min and Chen, Wenliang
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.
graph-based is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Hasan, Kazi Saidul and Ng, Vincent
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.
graph-based is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Huang, Hongzhao and Cao, Yunbo and Huang, Xiaojiang and Ji, Heng and Lin, Chin-Yew
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):
graph-based is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Saluja, Avneesh and Hassan, Hany and Toutanova, Kristina and Quirk, Chris
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).
graph-based is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Pilehvar, Mohammad Taher and Navigli, Roberto
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
graph-based is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Yan, Rui and Gao, Mingkun and Pavlick, Ellie and Callison-Burch, Chris
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.
graph-based is mentioned in 4 sentences in this paper.
Topics mentioned in this paper: