Index of papers in Proc. ACL that mention
  • graph-based
Nivre, Joakim and McDonald, Ryan
Abstract
In this paper, we show how these results can be exploited to improve parsing accuracy by integrating a graph-based and a transition-based model.
Integrated Models
For the graph-based model, X is the set of possible dependency arcs (2,7,1); for the transition-based model, X is the set of possible configuration-transition pairs (0,75).
Integrated Models
3.2 The Guided Graph-Based Model
Introduction
Practically all data-driven models that have been proposed for dependency parsing in recent years can be described as either graph-based or transition-based (McDonald and Nivre, 2007).
Introduction
In graph-based parsing, we learn a model for scoring possible dependency graphs for a given sentence, typically by factoring the graphs into their component arcs, and perform parsing by searching for the highest-scoring graph.
Introduction
The graph-based models are globally trained and use exact inference algorithms, but define features over a limited history of parsing decisions.
Two Models for Dependency Parsing
2.2 Graph-Based Models
Two Models for Dependency Parsing
Graph-based dependency parsers parameterize a model over smaller substructures in order to search the space of valid dependency graphs and produce the most likely one.
Two Models for Dependency Parsing
An advantage of graph-based methods is that tractable inference enables the use of standard structured learning techniques that globally set parameters to maximize parsing performance on the training set (McDonald et al., 2005a).
graph-based is mentioned in 24 sentences in this paper.
Topics mentioned in this paper:
Zeng, Xiaodong and Wong, Derek F. and Chao, Lidia S. and Trancoso, Isabel
Abstract
This paper introduces a graph-based semi-supervised joint model of Chinese word segmentation and part-of-speech tagging.
Abstract
The proposed approach is based on a graph-based label propagation technique.
Background
3.2 Graph-based Label Propagation
Background
Graph-based label propagation, a critical subclass of semi-supervised learning (SSL), has been widely used and shown to outperform other SSL methods (Chapelle et al., 2006).
Background
Typically, graph-based label propagation algorithms are run in two main steps: graph construction and label propagation.
Introduction
This study focuses on using a graph-based label propagation method to build a semi-supervised joint S&T model.
Introduction
Graph-based label propagation methods have recently shown they can outperform the state-of-the-art in several natural language processing (NLP) tasks, e.g., POS tagging (Subramanya et al., 2010), knowledge acquisition (Talukdar et al., 2008), shallow semantic parsing for unknown predicate (Das and Smith, 2011).
Introduction
Motivated by the works in (Subramanya et al., 2010; Das and Smith, 2011), for structured problems, graph-based label propagation can be employed to infer valuable syntactic information (n-gram-level label distributions) from labeled data to unlabeled data.
Method
The proposed approach employs a transductive graph-based label propagation method to acquire such gainful information, i.e., label distributions from a similarity graph constructed over labeled and unlabeled data.
Related Work
(2010) proposed a graph-based self-train style semi-supervised CRFs algorithm.
graph-based is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Guinaudeau, Camille and Strube, Michael
Abstract
We propose a computationally efficient graph-based approach for local coherence modeling.
Experiments
We evaluate the ability of our graph-based model to estimate the local coherence of a textual document with three different experiments.
Experiments
3Our graph-based model obtains for the discrimination task an accuracy of 0.846 and 0.635 on the ACCIDENTS and EARTHQUAKES datasets, respectively, compared to 0.904 and 0.872 as reported by Barzilay and Lapata (2008).
Experiments
Table 3: Discrimination, reproduced baselines (B&L: Barzilay and Lapata (2008); E&C Elsner and Charniak (2011)) vs. graph-based
Introduction
Similar to the application of graph-based methods in other areas of NLP (e.g.
Introduction
work on word sense disambiguation by Navigli and Lapata (2010); for an overview over graph-based methods in NLP see Mihalcea and Radev (2011)) we model local coherence by relying only on centrality measures applied to the nodes in the graph.
Introduction
We apply our graph-based model to the three tasks handled by Barzilay and Lapata (2008) to show that it provides the same flexibility over disparate tasks as the entity grid model: sentence ordering (Section 4.1), summary coherence ranking (Section 4.2), and readability assessment (Section 4.3).
Method
In contrast to Barzilay and Lapata’s entity grid that contains information about absent entities, our graph-based representation only contains “positive” information.
Method
From this graph-based representation, the local coherence of a text T can be measured by computing the average outdegree of a projection graph P. This centrality measure was chosen for two main reasons.
graph-based is mentioned in 19 sentences in this paper.
Topics mentioned in this paper:
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:
Liu, Shujie and Li, Chi-Ho and Li, Mu and Zhou, Ming
Abstract
We convert such graph-based translation consensus from similar source strings into useful features both for n-best output re-ranking and for decoding algorithm.
Graph-based Structured Learning
In general, a graph-based model assigns labels to instances by considering the labels of similar instances.
Graph-based Structured Learning
The gist of graph-based model is that, if two instances are connected by a strong edge, then their labels tend to be the same (Zhu, 2005).
Graph-based Structured Learning
This scenario differs from the general case of graph-based model in two aspects.
Graph-based Translation Consensus
Our MT system with graph-based translation consensus adopts the conventional log-linear model.
Graph-based Translation Consensus
Based on the commonly used features, two kinds of feature are added to equation (1), one is graph-based consensus features, which are about consensus among the translations of similar sentences/spans; the other is local consensus features, which are about consensus among the translations of the same sentence/span.
Introduction
Alexandrescu and Kirchhoff (2009) proposed a graph-based semi-supervised model to re-rank n-best translation output.
Introduction
In this paper, we attempt to leverage translation consensus among similar (spans of) source sentences in bilingual training data, by a novel graph-based model of translation consensus.
graph-based is mentioned in 30 sentences in this paper.
Topics mentioned in this paper:
Kolomiyets, Oleksandr and Bethard, Steven and Moens, Marie-Francine
Abstract
We compare two parsing models for temporal dependency structures, and show that a deterministic non-projective dependency parser outperforms a graph-based maximum spanning tree parser, achieving labeled attachment accuracy of 0.647 and labeled tree edit distance of 0.596.
Discussion and Conclusions
Comparing the two dependency parsing models, we have found that a shift-reduce parser, which more closely mirrors the incremental processing of our human annotators, outperforms a graph-based maximum spanning tree parser.
Evaluations
Table 2: Features for the shift-reduce parser (SRP) and the graph-based maximum spanning tree (MST) parser.
Evaluations
The Shift-Reduce parser (SRP; Section 4.1) and the graph-based , maximum spanning tree parser (MST; Section 4.2) are compared to these baselines.
Evaluations
It has been argued that graph-based models like the maximum spanning tree parser should be able to produce more globally consistent and correct dependency trees, yet we do not observe that here.
Feature Design
The shift-reduce parser (SRP) trains a machine learning classifier as the oracle 0 E (C —> T) to predict a transition 75 from a parser configuration 0 2 (L1, L2, Q, E), using node features such as the heads of L1, L2 and Q, and edge features from the already predicted temporal relations in E. The graph-based maximum spanning tree (MST) parser trains a machine learning model to predict SCORE(e) for an edge e = (107;, rj, wk), using features of the nodes w, and wk.
Parsing Models
We consider two different approaches to learning a temporal dependency parser: a shift-reduce model (Nivre, 2008) and a graph-based model (McDonald et al., 2005).
Parsing Models
4.2 Graph-Based Parsing Model
Parsing Models
Graph-based models are an alternative dependency parsing model, which assembles a graph with weighted edges between all pairs of words, and selects the tree-shaped subset of this graph that gives the highest total score (Fig.
graph-based is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Kim, Seokhwan and Lee, Gary Geunbae
Abstract
This paper proposes a novel graph-based projection approach and demonstrates the merits of it by using a Korean relation extraction system based on projected dataset from an English—Korean parallel corpus.
Cross-lingual Annotation Projection for Relation Extraction
To solve both of these problems at once, we propose a graph-based projection approach for relation extraction.
Graph Construction
The most crucial factor in the success of graph-based learning approaches is how to construct a graph that is appropriate for the target task.
Graph Construction
Das and Petrov (Das and Petrov, 2011) proposed a graph-based bilingual projection of part-of-speech tagging by considering the tagged words in the source language as labeled examples and connecting them to the unlabeled words in the target language, while referring to the word alignments.
Graph Construction
The graph for our graph-based projection is constructed by connecting related vertex pairs by weighted edges.
Implementation
To demonstrate the effectiveness of the graph-based projection approach for relation extraction, we developed a Korean relation extraction system that was trained with projected annotations from English resources.
Implementation
Table 1: Comparison between direct and graph-based projection approaches to extract semantic relationships for four relation types
Implementation
The graph-based projection was performed by the Junto toolkit 4 with the maximum number of iterations of 10 for each execution.
Introduction
In this paper, we propose a graph-based projection approach for weakly supervised relation extraction.
Introduction
The goal of our graph-based approach is to improve the robustness of the extractor with respect to errors that are generated and accumulated by preprocessors.
Label Propagation
To induce labels for all of the unlabeled vertices on the graph constructed in Section 3, we utilize the label propagation algorithm (Zhu and Ghahramani, 2002), which is a graph-based semi-supervised learning algorithm.
graph-based is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Chen, Wenliang and Zhang, Min and Li, Haizhou
Abstract
Most previous graph-based parsing models increase decoding complexity when they use high-order features due to exact-inference decoding.
Abstract
In this paper, we present an approach to enriching high—order feature representations for graph-based dependency parsing models using a dependency language model and beam search.
Experiments
Table 7 shows the performance of the graph-based systems that were compared, where McDonald06 refers to the second-order parser of McDonald
Implementation Details
We implement our parsers based on the MSTParserl, a freely available implementation of the graph-based model proposed by (McDonald and Pereira, 2006).
Introduction
Among them, graph-based dependency parsing models have achieved state-of-the-art performance for a wide range of Ian-guages as shown in recent CoNLL shared tasks
Introduction
In the graph-based models, dependency parsing is treated as a structured prediction problem in which the graphs are usually represented as factored structures.
Introduction
How to enrich high-order feature representations without increasing the decoding complexity for graph-based models becomes a very challenging problem in the dependency parsing task.
Parsing with dependency language model
3.1 Graph-based parsing model
Parsing with dependency language model
The graph-based parsing model aims to search for the maximum spanning tree (MST) in a graph (McDonald et al., 2005).
graph-based is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Wang, Dong and Liu, Yang
Abstract
The second one is a graph-based method, which incorporates topic and sentiment information, as well as additional information about sentence-to-sentence relations extracted based on dialogue structure.
Abstract
In particular, we find that incorporating dialogue structure in the graph-based method contributes to the improved system performance.
Experiments
In the graph-based method, the best parameters are Asim = 0,)\adj = 0.3,Arel = 0.4,Asent = 0.3.
Experiments
This is different from graph-based summarization systems for text domains.
Experiments
When compared to abstractive reference summaries, the graph-based method is slightly better.
Introduction
widely used in extractive summarization: sentence-ranking and graph-based methods.
Introduction
Furthermore, in the graph-based method, we propose to better incorporate the dialogue structure information in the graph in order to select salient summary utterances.
Opinion Summarization Methods
The second one is a graph-based method, which incorporates the dialogue structure in ranking.
Opinion Summarization Methods
4.2 Graph-based Summarization
Opinion Summarization Methods
Graph-based methods have been widely used in document summarization.
graph-based is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Celikyilmaz, Asli and Thint, Marcus and Huang, Zhiheng
Abstract
We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences.
Abstract
With a new representation of graph-based SSL on QA datasets using only a handful of features, and under limited amounts of labeled data, we show improvement in generalization performance over state-of-the-art QA models.
Conclusions and Discussions
In this paper, we applied a graph-based SSL algorithm to improve the performance of QA task by exploiting unlabeled entailment relations between affirmed question and candidate sentence pairs.
Conclusions and Discussions
We demonstrated that summarization on graph-based SSL can improve the QA task performance when more unlabeled data is used to learn the classifier model.
Experiments
We evaluated the performance of graph-based QA system using a set of 202 questions from the TREC04 as testing dataset (Voorhees, 2003), (Prager et al., 2000).
Graph Based Semi-Supervised Learning for Entailment Ranking
In general graph-based SSL, a function over the graph is estimated such that it satisfies two conditions: 1) close to the observed labels , and 2) be smooth on the whole graph by:
Graph Based Semi-Supervised Learning for Entailment Ranking
Most graph-based SSLs are transductive, i.e., not easily expendable to new test points outside L U U.
Graph Summarization
Research on graph-based SSL algorithms point out their effectiveness on real applications, e.g., (Zhu et al., 2003), (Zhou and Scholkopf, 2004), (Sindhwani et al., 2007).
Graph Summarization
Using graph-based SSL method on the new representative dataset, X’ = X U XTe, which is comprised of summarized dataset, X = {Xifizy as labeled data points, and the testing dataset, XTe as unlabeled data points.
Introduction
Recent research indicates that using labeled and unlabeled data in semi-supervised learning (SSL) environment, with an emphasis on graph-based methods, can improve the performance of information extraction from data for tasks such as question classification (Tri et al., 2006), web classification (Liu et al., 2006), relation extraction (Chen et al., 2006), passage-retrieval (Otterbacher et al., 2009), various natural language processing tasks such as part-of-speech tagging, and named-entity recognition (Suzuki and Isozaki, 2008), word-sense disam-
Introduction
We construct a textual entailment (TE) module by extracting features from each paired question and answer sentence and designing a classifier with a novel yet feasible graph-based SSL method.
graph-based is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Jiang, Long and Yu, Mo and Zhou, Ming and Liu, Xiaohua and Zhao, Tiejun
Approach Overview
Graph-based optimization as the third step to further boost the performance by taking the related tweets into consideration.
Conclusions and Future Work
In addition, different from previous work using only information on the current tweet for sentiment classification, we propose to take the related tweets of the current tweet into consideration by utilizing graph-based optimization.
Conclusions and Future Work
According to the experimental results, the graph-based optimization significantly improves the performance.
Experiments
6.4 Evaluation of Graph-based Optimization
Experiments
For these tweets, our graph-based optimization approach will have no effect.
Experiments
Target-dependent sentiment classifier +Graph-based optimization
Graph-based Sentiment Optimization
If we consider that the sentiment of a tweet only depends on its content and immediate neighbors, we can leverage a graph-based method for sentiment classification of tweets.
graph-based is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Celikyilmaz, Asli and Hakkani-Tur, Dilek and Tur, Gokhan and Sarikaya, Ruhi
Experiments
* SSL-Graph: A SSL model presented in (Subramanya et al., 2010) that uses graph-based leam-ing as posterior tag smoother for CRF model using Eq.
Experiments
For graph-based learning, we implemented the algorithm presented in (Subramanya et al., 2010) and used the same hyper-parameters and features.
Related Work and Motivation
Recent adaptation methods for SSL use: expectation minimization (Daumé-III, 2010) graph-based learning (Chapelle et al., 2006; Zhu, 2005), etc.
Related Work and Motivation
In (Subramanya et al., 2010) an efficient iterative SSL method is described for syntactic tagging, using graph-based learning to smooth POS tag posteriors.
Semi-Supervised Semantic Labeling
The unlabeled POS tag posteriors are then smoothed using a graph-based learning algorithm.
Semi-Supervised Semantic Labeling
Graph-based SSL defines a new CRF objective function:
Semi-Supervised Semantic Labeling
smoothing model, instead of a graph-based model, as follows:
graph-based is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Das, Dipanjan and Petrov, Slav
Abstract
We use graph-based label propagation for cross-lingual knowledge transfer and use the projected labels as features in an unsupervised model (Berg—Kirkpatrick et al., 2010).
Conclusion
We have shown the efficacy of graph-based label propagation for projecting part-of-speech information across languages.
Experiments and Results
To provide a thorough analysis, we evaluated three baselines and two oracles in addition to two variants of our graph-based approach.
Experiments and Results
We tried two versions of our graph-based approach:
Graph Construction
In graph-based learning approaches one constructs a graph whose vertices are labeled and unlabeled examples, and whose weighted edges encode the degree to which the examples they link have the same label (Zhu et al., 2003).
Graph Construction
Note, however, that it would be possible to use our graph-based framework also for completely unsupervised POS induction in both languages, similar to Snyder et al.
Introduction
First, we use a novel graph-based framework for projecting syntactic information across language boundaries.
graph-based is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Martins, Andre and Smith, Noah and Xing, Eric
Dependency Parsing
There has been much recent work on dependency parsing using graph-based , transition-based, and hybrid methods; see Nivre and McDonald (2008) for an overview.
Dependency Parsing
Typical graph-based methods consider linear classifiers of the form
Dependency Parsing as an ILP
Our approach will build a graph-based parser without the drawback of a restriction to local features.
Experiments
baselines, all of them state-of-the-art parsers based on non-arc-factored models: the second order model of McDonald and Pereira (2006), the hybrid model of Nivre and McDonald (2008), which combines a (labeled) transition-based and a graph-based parser, and a refinement of the latter, due to Martins et al.
Experiments
Comparing with the baselines, we observe that our full model outperforms that of McDonald and Pereira (2006), and is in line with the most accurate dependency parsers (Nivre and McDonald, 2008; Martins et al., 2008), obtained by combining transition-based and graph-based parsers.14 Notice that our model, compared with these hybrid parsers, has the advantage of not requiring an ensemble configuration (eliminating, for example, the need to tune two parsers).
Experiments
13Unlike our model, the hybrid models used here as baselines make use of the dependency labels at training time; indeed, the transition-based parser is trained to predict a labeled dependency parse tree, and the graph-based parser use these predicted labels as input features.
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:
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:
Razmara, Majid and Siahbani, Maryam and Haffari, Reza and Sarkar, Anoop
Experiments & Results 4.1 Experimental Setup
For evaluating our baseline as well as graph-based approaches, we use both intrinsic and extrinsic evaluations.
Experiments & Results 4.1 Experimental Setup
4.3.1 Graph-based Results
Graph-based Lexicon Induction
Graph-based approaches can easily become com-putationally very expensive as the number of nodes grow.
Related work
(2010) used linguistic analysis in the form of graph-based models instead of a vector space.
Related work
Graph-based semi-supervised methods have been shown to be useful for domain adaptation in MT as well.
Related work
Alexandrescu and Kirchhoff (2009) applied a graph-based method to determine similarities between sentences and use these similarities to promote similar translations for similar sentences.
graph-based is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Tanigaki, Koichi and Shiba, Mitsuteru and Munaka, Tatsuji and Sagisaka, Yoshinori
Introduction
In recent years, graph-based methods have attracted considerable attentions (Mihalcea, 2005; Navigli and Lapata, 2007; Agirre and Soroa, 2009).
Introduction
On the graph structure of lexical knowledge base (LKB), random-walk or other well-known graph-based techniques have been applied to find mutually related senses among target words.
Introduction
Unlike earlier studies disambiguating word-by-word, the graph-based methods obtain sense-interdependent solution for target words.
Related Work
As described in Section 1, graph-based WSD has been extensively studied, since graphs are favorable structure to deal with interactions of data on vertices.
Related Work
Our method can be viewed as one of graph-based methods, but it regards input-t0-class mapping as vertices, and the edges represent the relations both together in context and in sense.
Related Work
Mihalcea (2005) proposed graph-based methods, whose vertices are sense label hypotheses on word sequence.
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:
Jiang, Wenbin and Liu, Qun
Related Works
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.
Related Works
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.
Related Works
From this point, our model can be classified into the graph-based category.
Word-Pair Classification Model
Previous graph-based dependency models usually use the index distance of word 7' and word j
graph-based is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Martschat, Sebastian
A Multigraph Model
Work on graph-based models similar to ours report robustness with regard to the amount of training data used (Cai et al., 2011b; Cai et al., 2011a; Martschat et al., 2012).
Conclusions and Future Work
We presented an unsupervised graph-based model for coreference resolution.
Introduction
In this paper we present a graph-based approach for coreference resolution that models a document to be processed as a graph.
Related Work
Graph-based coreference resolution.
Related Work
While not developed within a graph-based framework, factor-based approaches for pronoun resolution (Mitkov, 1998) can be regarded as greedy clustering in a multigraph, where edges representing factors for pronoun resolution have negative or positive weight.
graph-based is mentioned in 5 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:
Carenini, Giuseppe and Ng, Raymond T. and Zhou, Xiaodong
Abstract
Second, we use two graph-based summarization approaches, Generalized ClueWordSummarizer and Page-Rank, to extract sentences as summaries.
Abstract
Third, we propose a summarization approach based on subjective opinions and integrate it with the graph-based ones.
Related Work
Finally, we did not compared CWS to other possible graph-based approaches as we propose in this paper.
Summarization with Subjective Opinions
Other than the conversation structure, the measures of cohesion and the graph-based summarization methods we have proposed, the importance of a sentence in emails can be captured from other aspects.
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:
Feng, Song and Kang, Jun Seok and Kuznetsova, Polina and Choi, Yejin
Connotation Induction Algorithms
Limitations of Graph-based Algorithms
Connotation Induction Algorithms
Although graph-based algorithms (§2.l, §2.2) provide an intuitive framework to incorporate various lexical relations, limitations include:
Connotation Induction Algorithms
Addressing limitations of graph-based algorithms (§2.2), we propose an induction algorithm based on Integer Linear Programming (ILP).
Experimental Result I
The [OVERLAY], which is based on both Pred-Arg and Arg-Arg subgraphs (§2.2), achieves the best performance among graph-based algorithms, significantly improving the precision over all other baselines.
graph-based is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Litvak, Marina and Last, Mark and Friedman, Menahem
MUSE — MUltilingual Sentence Extractor
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).
Related Work
Today, graph-based text representations are becoming increasingly popular, due to their ability to enrich the document model with syntactic and semantic relations.
Related Work
(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.
Related Work
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).
graph-based is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Xu, Liheng and Liu, Kang and Lai, Siwei and Chen, Yubo and Zhao, Jun
Related Work
There were also many works employed graph-based method (Li et al., 2012; Zhang et al., 2010; Hassan and Radev, 2010; Liu et al., 2012), but none of previous works considered confidence of patterns in the graph.
The First Stage: Sentiment Graph Walking Algorithm
In the first stage, we propose a graph-based algorithm called Sentiment Graph Walking to mine opinion words and opinion targets from reviews.
The First Stage: Sentiment Graph Walking Algorithm
We can see that our graph-based methods (Ours-Bigraph and 0urs-Stage1 ) achieve higher recall than Zhang.
graph-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Liu, Kang and Xu, Liheng and Zhao, Jun
Experiments
In such situation, the graph-based ranking algorithm in the second component will be apt to be affected by the frequency information, so the final performance could not be sensitive to the performance of opinion relations iden-
Opinion Target Extraction Methodology
To extract opinion targets from reviews, we adopt the framework proposed by (Liu et al., 2012), which is a graph-based extraction framework and
Opinion Target Extraction Methodology
In the second component, we adopt a graph-based algorithm used in (Liu et al., 2012) to compute the confidence of each opinion target candidate, and the candidates with higher confidence than the threshold will be extracted as the opinion targets.
graph-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Dasgupta, Anirban and Kumar, Ravi and Ravi, Sujith
Conclusions
We introduced a new general-purpose graph-based summarization framework that combines a submodular coverage function with a non-submodular dispersion function.
Introduction
We propose a very general graph-based summarization framework that combines a submodular function with a non-submodular dispersion function.
Related Work
Graph-based methods have been used for summarization (Ganesan et al., 2010), but in a different context—using paths in graphs to produce very short abstractive summaries.
graph-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Choi, Jinho D. and McCallum, Andrew
Experiments
The second block shows results from other kinds of parsing approaches (e.g., graph-based parsing, ensemble parsing, linear programming, dual decomposition).
Experiments
Our parser gives a comparative accuracy to Koo and Collins (2010) that is a 3rd-order graph-based parsing approach.
Experiments
Nivre and McDonald (2008) uses an ensemble model between transition-based and graph-based parsing approaches.
graph-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Scheible, Christian
Abstract
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.
Bilingual Lexicon Induction
Two examples of such methods are a graph-based approach by Dorow et al.
Bilingual Lexicon Induction
In this paper, we will employ the graph-based method.
graph-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Chen, Wenliang and Kazama, Jun'ichi and Torisawa, Kentaro
Dependency parsing
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).
Dependency parsing
In this paper, we employ the graph-based MST parsing model proposed by McDonald and Pereira
Dependency parsing
In the graph-based parsing model, features are represented for all the possible relations on single edges (two words) or adjacent edges (three words).
graph-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: