REEL: A Relation Extraction Learning framework

Barrio, Pablo; Simões, Gonçalo; Galhardas, Héléna; Gravano, Luis

doi:10.1109/jcdl.2014.6970222

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The fine-grained annotations offer more direct training signals to NER models but also bring challenges because more label classes need to be learned. In this section, we present our CogNN model that takes advantages of the fine-grained annotations to recognise person names 4 . Given a sequence of input tokens X, where X = [x 1 , x 2 , ..., x n ] and n is the length of the sequence, our aim is to predict for each token x i whether it is a name token.…”

Section: Proposed Models 41 Cognn Modelmentioning

confidence: 99%

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NameRec*: Highly Accurate and Fine-grained Person Name Recognition

Zhang¹,

Dai²,

Liu³

2021

Preprint

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In this paper, we introduce the NameRec* task, which aims to do highly accurate and fine-grained person name recognition. Traditional Named Entity Recognition models have good performance in recognising well-formed person names from text with consistent and complete syntax, such as news articles. However, there are rapidly growing scenarios where sentences are of incomplete syntax and names are in various forms such as user-generated contents and academic homepages. To address person name recognition in this context, we propose a fine-grained annotation scheme based on anthroponymy. To take full advantage of the fine-grained annotations, we propose a Co-guided Neural Network (CogNN) for person name recognition. CogNN fully explores the intra-sentence context and rich training signals of name forms. To better utilize the inter-sentence context and implicit relations, which are extremely essential for recognizing person names in long documents, we further propose an Inter-sentence BERT Model (IsBERT).IsBERT has an overlapped input processor, and an inter-sentence encoder with bidirectional overlapped contextual embedding learning and multi-hop inference mechanisms. To derive benefit from different documents with a diverse abundance of context, we propose an advanced Adaptive Inter-sentence BERT Model (Ada-IsBERT) to dynamically adjust the inter-sentence overlapping ratio to different documents. We conduct extensive experiments to demonstrate the superiority of the proposed methods on both academic homepages and news articles.

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Section: Proposed Models 41 Cognn Modelmentioning

confidence: 99%

“…Person name recognition can also provide valuable insights for learning the relationships between people and provides valuable insights for analysing their collaboration networks. [4], [5].…”

Section: Introductionmentioning

confidence: 99%

NameRec*: Highly Accurate and Fine-grained Person Name Recognition

Zhang¹,

Dai²,

Liu³

2021

Preprint

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“…• Relation Extraction: To extract our relations, we trained relation extraction systems using REEL 7 (Barrio et al, 2014). The two best performing systems, and the ones that we use in our experiments, are Subsequence Kernel (Bunescu and Mooney, 2005) (SSK) and Bag of n-grams (Giuliano et al, 2006) (BONG).…”

Section: Experimental Settingsmentioning

confidence: 99%

Sampling strategies for information extraction over the deep web

Barrio

Gravano

2017

Information Processing & Management

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Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections-and in which order-to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deepweb text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.

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“…Information Extraction Systems: We evaluated a variety of IE systems and components for all relations in our experiments (see below) via 5-fold crossvalidation over a set of training documents, and selected the two best-performing combinations, namely, Subsequence Kernel (SSK) [9] and Bag of n-Grams (BONG) [14]. We implemented them using REEL 15 [5]. We also considered different named entity taggers and selected: for person and location entities, the pretrained conditional random fields (CRF) [26] from the StanfordNLP package 16 ; for natural disasters, CRFs from the etxt2db framework 17 ; for the remaining entities, maximum entropy markov models (MEMM) [25], also from etxt2db.…”

Section: Experimental Settingsmentioning

confidence: 99%

Ranking Deep Web Text Collections for Scalable Information Extraction

Barrio

Gravano

Develder

2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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Information extraction (IE) systems discover structured information from natural language text, to enable much richer querying and data mining than possible directly over the unstructured text. Unfortunately, IE is generally a computationally expensive process, and hence improving its efficiency, so that it scales over large volumes of text, is of critical importance. State-of-the-art approaches for scaling the IE process focus on one text collection at a time. These approaches prioritize the extraction effort by learning keyword queries to identify the "useful" documents for the IE task at hand, namely, those that lead to the extraction of structured "tuples." These approaches, however, do not attempt to predict which text collections are useful for the IE task-and hence merit further processing-and which ones will not contribute any useful output-and hence should be ignored altogether, for efficiency. In this paper, we focus on an especially valuable family of text sources, the so-called deep web collections, whose (remote) contents are only accessible via querying. Specifically, we introduce and study techniques for ranking deep web collections for an IE task, to prioritize the extraction effort by focusing on collections with substantial numbers of useful documents for the task. We study both (adaptations of) state-of-the-art resource selection strategies for distributed information retrieval, and IE-specific approaches. Our extensive experimental evaluation over realistic deep web collections, and for several different IE tasks, shows the merits and limitations of the alternative families of approaches, and provides a roadmap for addressing this critically important building block for efficient, scalable information extraction.

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REEL: A Relation Extraction Learning framework

Cited by 6 publications

References 5 publications

NameRec*: Highly Accurate and Fine-grained Person Name Recognition

NameRec*: Highly Accurate and Fine-grained Person Name Recognition

Sampling strategies for information extraction over the deep web

Ranking Deep Web Text Collections for Scalable Information Extraction

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