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Modeling relations and their mentions without labeled text
- In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part III
, 2010
"... Abstract. Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the ..."
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Abstract. Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a state-of-the-art approach for relation extraction under distant supervision, we achieve 31 % error reduction. 1
In-domain Relation Discovery with Meta-constraints via Posterior Regularization
"... We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that in ..."
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We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single relation should exhibit regularities at multiple levels of linguistic structure, including lexicography, syntax, and document-level context. We capture these regularities via the structure of our probabilistic model as well as a set of declaratively-specified constraints enforced during posterior inference. Across two domains our approach successfully recovers hidden relation structure, comparable to or outperforming previous state-of-the-art approaches. Furthermore, we find that a small set of constraints is applicable across the domains, and that using domain-specific constraints can further improve performance. 1 1
Multi event extraction guided by global constraints
- In Proceedings of NAACL-HLT
, 2012
"... This paper addresses the extraction of event records from documents that describe multiple events. Specifically, we aim to identify the fields of information contained in a document and aggregate together those fields that describe the same event. To exploit the inherent connections between field ex ..."
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Cited by 5 (0 self)
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This paper addresses the extraction of event records from documents that describe multiple events. Specifically, we aim to identify the fields of information contained in a document and aggregate together those fields that describe the same event. To exploit the inherent connections between field extraction and event identification, we propose to model them jointly. Our model is novel in that it integrates information from separate sequential models, using global potentials that encourage the extracted event records to have desired properties. While the model contains high-order potentials, efficient approximate inference can be performed with dualdecomposition. We experiment with two data sets that consist of newspaper articles describing multiple terrorism events, and show that our model substantially outperforms traditional pipeline models. 1
Linking Named Entities to Any Database
- In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL
, 2012
"... Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a ..."
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Cited by 4 (1 self)
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Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a new task, called Open-Database Named-Entity Disambiguation (Open-DB NED), in which a system must be able to resolve named entities to symbols in an arbitrary database, without requiring labeled data for each new database. We introduce two techniques for Open-DB NED, one based on distant supervision and the other based on domain adaptation. In experiments on two domains, one with poor coverage by Wikipedia and the other with near-perfect coverage, our Open-DB NED strategies outperform a state-of-the-art Wikipedia NED system by over 25 % in accuracy.
Ontological smoothing for relation extraction with minimal supervision. In AAAI. A Categories of SPARQL queries List of computer science conferences. Similar query can be written for other area conferences SELECT ?Conferences WHERE 57 ?Conferences rdf:typ
- SELECT ?ProgrammingLanguage ?version ?OperatingSystem WHERE { ?ProgrammingLanguage rdf:type . ?ProgrammingLanguage ?version . ?ProgrammingLanguage ?OperatingSystem . } LIMIT 100 Query
, 2012
"... Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations ’ arguments. Unfortunately, t ..."
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Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations ’ arguments. Unfortunately, these methods require numerous training examples, which are expensive and time-consuming to produce. This paper presents ontological smoothing, a semi-supervised technique that learns extractors for a set of minimally-labeled relations. Ontological smoothing has three phases. First, it generates a mapping between the target relations and a background knowledge-base. Second, it uses distant supervision to heuristically generate new training examples for the target relations. Finally, it learns an extractor from a combination of the original and newly-generated examples. Experiments on 65 relations across three target domains show that ontological smoothing can dramatically improve precision and recall, even rivaling fully supervised performance in many cases.
Learning Semantic Structures from In-domain Documents
, 2010
"... Semantic analysis is a core area of natural language understanding that has typically focused on predicting domain-independent representations. However, such representations are unable to fully realize the rich diversity of technical content prevalent in a variety of specialized domains. Taking the ..."
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Semantic analysis is a core area of natural language understanding that has typically focused on predicting domain-independent representations. However, such representations are unable to fully realize the rich diversity of technical content prevalent in a variety of specialized domains. Taking the standard supervised approach to domainspecific semantic analysis requires expensive annotation effort for each new domain of interest. In this thesis, we study how multiple granularities of semantic analysis can be learned from unlabeled documents within the same domain. By exploiting indomain regularities in the expression of text at various layers of linguistic phenomena, including lexicography, syntax, and discourse, the statistical approaches we propose induce multiple kinds of structure: relations at the phrase and sentence level, content models at the paragraph and section level, and semantic properties at the document level. Each of our models is formulated in a hierarchical Bayesian framework with the target structure captured as latent variables, allowing them to seamlessly incorporate
Distantly Labeling Data for Large Scale
, 2010
"... Abstract. Cross-document coreference, the problem of resolving entity men-tions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this ta ..."
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Abstract. Cross-document coreference, the problem of resolving entity men-tions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this task. In this paper we develop and demonstrate an approach based on “distantly-labeling ” a data set from which we can train a discriminative cross-document coreference model. In particular we build a dataset of more than a million people mentions extracted from 3.5 years of New York Times articles, leverage Wikipedia for distant label-ing with a generative model (and measure the reliability of such labeling); then we train and evaluate a conditional random field coreference model that has fac-tors on cross-document entities as well as mention-pairs. This coreference model obtains high accuracy in resolving mentions and entities that are not present in the training data, indicating applicability to non-Wikipedia data. Given the large amount of data, our work is also an exercise demonstrating the scalability of our approach.
Rich Prior Knowledge in Learning for NLP
"... have: unlabeled data option: hire linguist annotators Why Incorporate Prior Knowledge? have: unlabeled data option: hire linguist annotators This approach does not scale to every task and domain of interest. However, we already know a lot about most problems of interest. Example: Document Classifica ..."
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have: unlabeled data option: hire linguist annotators Why Incorporate Prior Knowledge? have: unlabeled data option: hire linguist annotators This approach does not scale to every task and domain of interest. However, we already know a lot about most problems of interest. Example: Document Classification • Prior Knowledge: • labeled features: information about the label distribution when word w is present
