Entity Set Expansion based on Bootstrapping Methods using Topic Information

Sadamitsu, Kugatsu; Saito, Kuniko; Imamura, Kenji; Matsuo, Yuichi; Kikui, Genichiro

doi:10.5715/jnlp.19.89

Cited by 4 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have presented various approaches to reduce the effects of semantic drift. The approaches range from refinement of the seed set , applying classifier (Bellare et al, 2007;Sadamitsu et al, 2011;Pennacchiotti and Pantel, 2011), using human judges , to using relationships between semantic categories (Curran et al, 2007;Carlson et al, 2010). investigated the influence of seed instances on bootstrapping algorithms.…”

Section: Approaches To Semantic Driftmentioning

confidence: 99%

See 1 more Smart Citation

Set Expansion Using Sibling Relationships Between Semantic Categories

Takase¹,

Okazaki²,

Inui³

2013

Journal of Natural Language Processing

View full text Add to dashboard Cite

Most set expansion algorithms are assumed to independently acquire new instances of each of the different semantic categories even when instances of multiple semantic categories are seeded. However, in the setting of set expansion with multiple semantic categories, we might leverage other types of prior knowledge about semantic categories. In this paper, we present a method of set expansion in case ontological information related to target semantic categories is available. Specifically, the proposed method uses sibling relationships between semantic categories as an additional type of prior knowledge. We demonstrate the effectiveness of using sibling relationships in set expansion on a dataset in which instances and sibling relationships are extracted from Wikipedia in a semi-automatic manner.

show abstract

Section: Approaches To Semantic Driftmentioning

confidence: 99%

“…The classifier approach can use multiple features to select instances. Sadamitsu et al (2011) extended the method of Bellare et al (2007) to use topic information estimated using Latent Dirichlet Allocation (LDA). They use not only contexts but also topic information as features of the classifier.…”

Section: Approaches To Semantic Driftmentioning

confidence: 99%

Set Expansion Using Sibling Relationships Between Semantic Categories

Takase¹,

Okazaki²,

Inui³

2013

Journal of Natural Language Processing

View full text Add to dashboard Cite

show abstract

“…Coupling the learning of category extractors by using positive examples of one category as negative examples for others has been shown to help limiting such a decline in accuracy [4]. Also, entity set expansion using topic information can alleviate semantic drift in bootstrapping entity set expansion [8].…”

Section: Related Workmentioning

confidence: 99%

“…It can be considered a Bayesian inference method that, when applied to exponential family models with conjugate priors, can be implemented using exact algorithms that tend to be computationally efficient. Recent studies [8,9] have shown, however, that the direct application of Bayesian Sets may produce poor results in tasks such as information extraction from text. In addition, when Bayesian Sets are applied to problems in which the number of labeled examples is too small, the induced results tend to be deteriorated.…”

Section: Introductionmentioning

confidence: 99%

Coupled Bayesian Sets Algorithm for Semi-supervised Learning and Information Extraction

Verma

Hruschka

2012

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

Our inspiration comes from Nell (Never Ending Language Learning), a computer program running at Carnegie Mellon University to extract structured information from unstructured web pages. We consider the problem of semi-supervised learning approach to extract category instances (e.g. country(USA), city(New York)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semisupervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce an internally consistent, but nevertheless, incorrect set of extractions. We believe that this problem can be overcome by simultaneously learning independent classifiers in a new approach named Coupled Bayesian Sets algorithm, based on Bayesian Sets, for many different categories and relations (in the presence of an ontology defining constraints that couple the training of these classifiers). Experimental results show that simultaneously learning a coupled collection of classifiers for random 11 categories resulted in much more accurate extractions than training classifiers through original Bayesian Sets algorithm, Naive Bayes, BaS-all and Coupled Pattern Learner (the category extractor used in NELL).

show abstract

“…There are a number of papers on various aspects of "set expansion," often for completing lists of entities from structured lists, like those extracted from Wikipedia (Sarmento et al 2007), using rules from natural language processing or topic models (Tran et al 2010;Sadamitsu et al 2011), or from opinion corpora (Zhang and Liu 2011). The task we explore here is web-based set expansion and methods developed for other set expansion tasks are not directly applicable.…”

Section: Related Workmentioning

confidence: 99%

Growing a list

Letham

Rudin

Heller

2013

Data Min Knowl Disc

View full text Add to dashboard Cite

It is easy to find expert knowledge on the Internet on almost any topic, but obtaining a complete overview of a given topic is not always easy: information can be scattered across many sources and must be aggregated to be useful. We introduce a method for intelligently growing a list of relevant items, starting from a small seed of examples. Our algorithm takes advantage of the wisdom of the crowd, in the sense that there are many experts who post lists of things on the Internet. We use a collection of simple machine learning components to find these experts and aggregate their lists to produce a single complete and meaningful list. We use experiments with gold standards and open-ended experiments without gold standards to show that our method significantly outperforms the state of the art. Our method uses the ranking algorithm Bayesian Sets even when its underlying independence assumption is violated, and we provide a theoretical generalization bound to motivate its use.

show abstract

Entity Set Expansion based on Bootstrapping Methods using Topic Information

Cited by 4 publications

References 16 publications

Set Expansion Using Sibling Relationships Between Semantic Categories

Set Expansion Using Sibling Relationships Between Semantic Categories

Coupled Bayesian Sets Algorithm for Semi-supervised Learning and Information Extraction

Growing a list

Contact Info

Product

Resources

About