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 |
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 |
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. |
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. |
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 . |
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 . |