Denilson Barbosa


2019

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KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
Filipe Mesquita | Matteo Cannaviccio | Jordan Schmidek | Paramita Mirza | Denilson Barbosa
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction). We discuss five baseline approaches, where the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79% (0.28). However, our best baseline is far from reaching human performance (0.82), indicating our dataset is challenging. The KnowledgeNet dataset and baselines are available at https://github.com/diffbot/knowledge-net

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Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction
Peng Xu | Denilson Barbosa
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. One way to improve RE is to use KB Embeddings (KBE) for link prediction. However, despite clear connections between RE and KBE, little has been done toward properly unifying these models systematically. We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. The code is available at https://github.com/billy-inn/HRERE.

2018

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Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss
Peng Xu | Denilson Barbosa
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross-entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task.

2014

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Improving Open Relation Extraction via Sentence Re-Structuring
Jordan Schmidek | Denilson Barbosa
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Information Extraction is an important task in Natural Language Processing, consisting of finding a structured representation for the information expressed in natural language text. Two key steps in information extraction are identifying the entities mentioned in the text, and the relations among those entities. In the context of Information Extraction for the World Wide Web, unsupervised relation extraction methods, also called Open Relation Extraction (ORE) systems, have become prevalent, due to their effectiveness without domain-specific training data. In general, these systems exploit part-of-speech tags or semantic information from the sentences to determine whether or not a relation exists, and if so, its predicate. This paper discusses some of the issues that arise when even moderately complex sentences are fed into ORE systems. A process for re-structuring such sentences is discussed and evaluated. The proposed approach replaces complex sentences by several others that, together, convey the same meaning and are more amenable to extraction by current ORE systems. The results of an experimental evaluation show that this approach succeeds in reducing the processing time and increasing the accuracy of the state-of-the-art ORE systems.

2013

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Identification of Speakers in Novels
Hua He | Denilson Barbosa | Grzegorz Kondrak
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Open Information Extraction with Tree Kernels
Ying Xu | Mi-Young Kim | Kevin Quinn | Randy Goebel | Denilson Barbosa
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Effectiveness and Efficiency of Open Relation Extraction
Filipe Mesquita | Jordan Schmidek | Denilson Barbosa
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Topic Classification of Blog Posts Using Distant Supervision
Stephanie Husby | Denilson Barbosa
Proceedings of the Workshop on Semantic Analysis in Social Media

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Automatic Evaluation of Relation Extraction Systems on Large-scale
Mirko Bronzi | Zhaochen Guo | Filipe Mesquita | Denilson Barbosa | Paolo Merialdo
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)