Index of papers in Proc. ACL 2012 that mention
  • relation extraction
Garrido, Guillermo and Peñas, Anselmo and Cabaleiro, Bernardo and Rodrigo, Álvaro
Abstract
Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually flaents, as their validity is naturally anchored to a certain time period.
Abstract
This paper proposes a methodological approach to temporally anchored relation extraction .
Abstract
Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step.
Distant Supervised Relation Extraction
To perform relation extraction , our proposal follows a distant supervision approach (Mintz et al., 2009), which has also inspired other slot filling systems (Agirre et al., 2009; Surdeanu et al., 2010).
Evaluation
Our system was one of the five that took part in the task.We have evaluated the overall system and the two main components of the architecture: Relation Extraction , and Temporal Anchoring of the relations.
Evaluation
6.1 Evaluation of Relation Extraction
Introduction
As pointed out in (Ling and Weld, 2010), while much research in automatic relation extraction has focused on distilling static facts from text, many of the target relations are in fact flaents, dynamic relations whose truth value is dependent on time (Russell and Norvig, 2010).
Introduction
The Temporally anchored relation extraction problem consists in, given a natural language text document corpus, C, a target entity, 6, and a target
Introduction
ed Relation Extraction
relation extraction is mentioned in 24 sentences in this paper.
Topics mentioned in this paper:
Kim, Seokhwan and Lee, Gary Geunbae
Abstract
Although researchers have conducted extensive studies on relation extraction in the last decade, supervised approaches are still limited because they require large amounts of training data to achieve high performances.
Abstract
To build a relation extractor without significant annotation effort, we can exploit cross-lingual annotation projection, which leverages parallel corpora as external resources for supervision.
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.
Introduction
Relation extraction aims to identify semantic relations of entities in a document.
Introduction
Although many supervised machine learning approaches have been successfully applied to relation extraction tasks (Ze-lenko et al., 2003; Kambhatla, 2004; Bunescu and Mooney, 2005; Zhang et al., 2006), applications of these approaches are still limited because they require a sufficient number of training examples to obtain good extraction results.
Introduction
Although these datasets encourage the development of relation extractors for these major languages, there are few labeled training samples for learning new systems in
relation extraction is mentioned in 23 sentences in this paper.
Topics mentioned in this paper:
Takamatsu, Shingo and Sato, Issei and Nakagawa, Hiroshi
Abstract
In relation extraction , distant supervision seeks to extract relations between entities from text by using a knowledge base, such as Freebase, as a source of supervision.
Abstract
In the experiment, we also found that our wrong label reduction boosted the performance of relation extraction .
Experiments
Experiment 2 aimed to evaluate how much our wrong label reduction in Section 4 improved the performance of relation extraction .
Introduction
Machine learning approaches have been developed to address relation extraction , which is the task of extracting semantic relations between entities expressed in text.
Introduction
0 We applied our method to Wikipedia articles using Freebase as a knowledge base and found that (i) our model identified patterns expressing a given relation more accurately than baseline methods and (ii) our method led to better extraction performance than the original DS (Mintz et al., 2009) and MultiR (Hoffmann et al., 2011), which is a state-of-the-art multi-instance learning system for relation extraction (see Section 7).
Knowledge-based Distant Supervision
In this section, we describe DS for relation extraction .
Knowledge-based Distant Supervision
Relation extraction seeks to extract relation instances from text.
Knowledge-based Distant Supervision
DS uses a knowledge base to create labeled data for relation extraction by heuristically matching entity pairs.
Related Work
(2009) who used Freebase as a knowledge base by making the DS assumption and trained relation extractors on Wikipedia.
Related Work
Bootstrapping for relation extraction (Riloff and Jones, 1999; Pantel and Pennacchiotti, 2006; Carlson et al., 2010) is related to our method.
Wrong Label Reduction
For relation extraction , we train a classifier for entity pairs using the resultant labeled data.
relation extraction is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Branavan, S.R.K. and Kushman, Nate and Lei, Tao and Barzilay, Regina
Abstract
In this paper, we express the semantics of precondition relations extracted from text in terms of planning operations.
Abstract
When applied to a complex virtual world and text describing that world, our relation extraction technique performs on par with a supervised baseline, yielding an F-measure of 66% compared to the baseline’s 65%.
Conclusions
While using planning feedback as its only source of supervision, our method for relation extraction achieves a performance on par with that of a supervised baseline.
Experimental Setup
Evaluation Metrics We use our manual annotations to evaluate the type-level accuracy of relation extraction .
Experimental Setup
Baselines To evaluate the performance of our relation extraction , we compare against an SVM classifier8 trained on the Gold Relations.
Introduction
The central idea of our work is to express the semantics of precondition relations extracted from text in terms of planning operations.
Introduction
We build on the intuition that the validity of precondition relations extracted from text can be informed by the execution of a low-level planner.3 This feedback can enable us to learn these relations without annotations.
Introduction
Our results demonstrate the strength of our relation extraction technique — while using planning feedback as its only source of supervision, it achieves a precondition relation extraction accuracy on par with that of a supervised SVM baseline.
Results
Relation Extraction Figure 5 shows the performance of our method on identifying preconditions in text.
relation extraction is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Alfonseca, Enrique and Filippova, Katja and Delort, Jean-Yves and Garrido, Guillermo
Conclusions
We have described a new distant supervision model with which to learn patterns for relation extraction with no manual intervention.
Experiments and results
In the case of nationality, however, even though the extracted sentences do not support the relation (P@50 = 0.34 for intertext), the new relations extracted are mostly correct (P@50 = 0.86) as most presidents and ministers in the real world have the nationality of the country where they govern.
Introduction
Open Information Extraction (Sekine, 2006; Banko et al., 2007; Bollegala et al., 2010) started as an effort to approach relation extraction in
Introduction
A different family of unsupervised methods for relation extraction is unsupervised semantic parsing, which aims at clustering entity mentions and relation surface forms, thus generating a semantic representation of the texts on which inference may be used.
Introduction
The main contribution of this work is presenting a variant of distance supervision for relation extraction where we do not use heuristics in the selection of the training data.
Unsupervised relational pattern learning
Figure 1: Example of a generated set of document collections from a news corpus for relation extraction .
relation extraction is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Yao, Limin and Riedel, Sebastian and McCallum, Andrew
Experiments
Rel-LDA: Generative models have been successfully applied to unsupervised relation extraction (Rink and Harabagiu, 2011; Yao et al., 2011).
Introduction
Relation extraction (RE) is the task of determining semantic relations between entities mentioned in text.
Introduction
Here, the relation extractor simultaneously discovers facts expressed in natural language, and the ontology into which they are assigned.
Related Work
Many generative probabilistic models have been applied to relation extraction .
relation extraction is mentioned in 4 sentences in this paper.
Topics mentioned in this paper: