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31
Syntactic and semantic structure for opinion expression detection
- In Proceedings of the 14th Conference on Computational Natural Language Learning
, 2010
"... We demonstrate that relational features derived from dependency-syntactic and semantic role structures are useful for the task of detecting opinionated expressions in natural-language text, significantly improving over conventional models based on sequence labeling with local features. These feature ..."
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Cited by 21 (4 self)
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We demonstrate that relational features derived from dependency-syntactic and semantic role structures are useful for the task of detecting opinionated expressions in natural-language text, significantly improving over conventional models based on sequence labeling with local features. These features allow us to model the way opinionated expressions interact in a sentence over arbitrary distances. While the relational features make the prediction task more computationally expensive, we show that it can be tackled effectively by using a reranker. We evaluate a number of machine learning approaches for the reranker, and the best model results in a 10-point absolute improvement in soft recall on the MPQA corpus, while decreasing precision only slightly. 1
Embedding semantic similarity in tree kernels for domain adaptation of relation extraction
- In ACL
, 2013
"... Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on rela-tion extraction are mostly supervised. The clear drawback of supervised methods is the need of training data: labeled data is expensive to obtain, and there is often a mismatch ..."
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Cited by 13 (4 self)
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Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on rela-tion extraction are mostly supervised. The clear drawback of supervised methods is the need of training data: labeled data is expensive to obtain, and there is often a mismatch between the training data and the data the system will be applied to. This is the problem of domain adapta-tion. In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic anal-ysis (LSA) and (ii) structured kernels to improve the adaptability of relation ex-tractors to new text genres/domains. The empirical evaluation on ACE 2005 do-mains shows that a suitable combination of syntax and lexical generalization is very promising for domain adaptation. 1
Joint Distant and Direct Supervision for Relation Extraction
"... Supervised approaches to Relation Extraction (RE) are characterized by higher accuracy than unsupervised models. Unfortunately, their applicability is limited by the need of training data for each relation type. Automatic creation of such data using Distant Supervision (DS) provides a promising solu ..."
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Cited by 10 (1 self)
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Supervised approaches to Relation Extraction (RE) are characterized by higher accuracy than unsupervised models. Unfortunately, their applicability is limited by the need of training data for each relation type. Automatic creation of such data using Distant Supervision (DS) provides a promising solution to the problem. In this paper, we study DS for designing endto-end systems of sentence-level RE. In particular, we propose a joint model between Web data derived with DS and manually annotated data from ACE. The results show (i) an improvement on the previous state-of-the-art in ACE, which provides important evidence of the benefit of DS; and (ii) a rather good accuracy on extracting 52 types of relations from Web data, which suggests the applicability of DS for general RE. 1
Convolution Kernels for Opinion Holder Extraction
"... Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use c ..."
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Cited by 10 (3 self)
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Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary. 1
Relation extraction with relation topics
- In EMNLP
, 2011
"... This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new ..."
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Cited by 9 (1 self)
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This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new relation’s training instances onto a lower dimension topic space constructed from existing relation detectors through a three step process. First, we construct a large relation repository of more than 7,000 relations from Wikipedia. Second, we construct a set of non-redundant relation topics defined at multiple scales from the relation repository to characterize the existing relations. Similar to the topics defined over words, each relation topic is an interpretable multinomial distribution over the existing relations. Third, we integrate the relation topics in a kernel function, and use it together with SVM to construct detectors for new relations. The experimental results on Wikipedia and ACE data have confirmed that backgroundknowledge-based topics generated from the Wikipedia relation repository can significantly improve the performance over the state-of-theart relation detection approaches. 1
Fourth-Order Dependency Parsing
, 2012
"... We present and implement a fourth-order projective dependency parsing algorithm that effectively utilizes both “grand-sibling ” style and “tri-sibling” style interactions of third-order and “grand-tri-sibling ” style interactions of forth-order factored parts for performance enhancement. This algori ..."
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Cited by 8 (5 self)
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We present and implement a fourth-order projective dependency parsing algorithm that effectively utilizes both “grand-sibling ” style and “tri-sibling” style interactions of third-order and “grand-tri-sibling ” style interactions of forth-order factored parts for performance enhancement. This algorithm requires O(n 5) time and O(n 4) space. We implement and evaluate the parser on two languages—English and Chinese, both achieving state-of-the-art accuracy. This results show that a higher-order (≥4) dependency parser gives performance improvement over all previous lower-order parsers.
A Study on Dependency Tree Kernels for Automatic Extraction of Protein-Protein Interaction
"... Kernel methods are considered the most effective techniques for various relation extraction (RE) tasks as they provide higher accuracy than other approaches. In this paper, we introduce new dependency tree (DT) kernels for RE by improving on previously proposed dependency tree structures. These are ..."
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Cited by 8 (4 self)
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Kernel methods are considered the most effective techniques for various relation extraction (RE) tasks as they provide higher accuracy than other approaches. In this paper, we introduce new dependency tree (DT) kernels for RE by improving on previously proposed dependency tree structures. These are further enhanced to design more effective approaches that we call mildly extended dependency tree (MEDT) kernels. The empirical results on the protein-protein interaction (PPI) extraction task on the AIMed corpus show that tree kernels based on our proposed DT structures achieve higher accuracy than previously proposed DT and phrase structure tree (PST) kernels. 1
Automatic Detection and Classification of Social Events
"... In this paper we introduce the new task of social event extraction from text. We distinguish two broad types of social events depending on whether only one or both parties are aware of the social contact. We annotate part of Automatic Content Extraction (ACE) data, and perform experiments using Supp ..."
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Cited by 7 (0 self)
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In this paper we introduce the new task of social event extraction from text. We distinguish two broad types of social events depending on whether only one or both parties are aware of the social contact. We annotate part of Automatic Content Extraction (ACE) data, and perform experiments using Support Vector Machines with Kernel methods. We use a combination of structures derived from phrase structure trees and dependency trees. A characteristic of our events (which distinguishes them from ACE events) is that the participating entities can be spread far across the parse trees. We use syntactic and semantic insights to devise a new structure derived from dependency trees and show that this plays a role in achieving the best performing system for both social event detection and classification tasks. We also use three data sampling approaches to solve the problem of data skewness. Sampling methods improve the F1-measure for the task of relation detection by over 20 % absolute over the baseline. 1
Kernel-based Reranking for Named-Entity Extraction
"... We present novel kernels based on structured and unstructured features for reranking the N-best hypotheses of conditional random fields (CRFs) applied to entity extraction. The former features are generated by a polynomial kernel encoding entity features whereas tree kernels are used to model depend ..."
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Cited by 6 (2 self)
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We present novel kernels based on structured and unstructured features for reranking the N-best hypotheses of conditional random fields (CRFs) applied to entity extraction. The former features are generated by a polynomial kernel encoding entity features whereas tree kernels are used to model dependencies amongst tagged candidate examples. The experiments on two standard corpora in two languages, i.e. the Italian EVALITA 2009 and the English CoNLL 2003 datasets, show a large improvement on CRFs in F-measure, i.e. from 80.34 % to 84.33 % and from 84.86% to 88.16%, respectively. Our analysis reveals that both kernels provide a comparable improvement over the CRFs baseline. Additionally, their combination improves CRFs much more than the sum of the individual contributions, suggesting an interesting kernel synergy. 1
A.: On reverse feature engineering of syntactic tree kernels
- In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning
, 2010
"... In this paper, we provide a theoretical framework for feature selection in tree kernel spaces based on gradient-vector components of kernel-based machines. We show that a huge number of features can be discarded without a significant decrease in accuracy. Our selection algorithm is as accurate as an ..."
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Cited by 6 (0 self)
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In this paper, we provide a theoretical framework for feature selection in tree kernel spaces based on gradient-vector components of kernel-based machines. We show that a huge number of features can be discarded without a significant decrease in accuracy. Our selection algorithm is as accurate as and much more efficient than those proposed in previous work. Comparative experiments on three interesting and very diverse classification tasks, i.e. Question Classification, Relation Extraction and Semantic Role Labeling, support our theoretical findings and demonstrate the algorithm performance. 1
