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Jiang Guo

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5 papers

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AAAI Conference 2018 Conference Paper

A Neural Transition-Based Approach for Semantic Dependency Graph Parsing

Yuxuan Wang
Wanxiang Che
Jiang Guo
Ting Liu

Semantic dependency graph has been recently proposed as an extension of tree-structured syntactic or semantic representation for natural language sentences. It particularly features the structural property of multi-head, which allows nodes to have multiple heads, resulting in a directed acyclic graph (DAG) parsing problem. Yet most statistical parsers focused exclusively on shallow bi-lexical tree structures, DAG parsing remains under-explored. In this paper, we propose a neural transition-based parser, using a variant of list-based arc-eager transition algorithm for dependency graph parsing. Particularly, two non-trivial improvements are proposed for representing the key components of the transition system, to better capture the semantics of segments and internal sub-graph structures. We test our parser on the SemEval-2016 Task 9 dataset (Chinese) and the SemEval-2015 Task 18 dataset (English). On both benchmark datasets, we obtain superior or comparable results to the best performing systems. Our parser can be further improved with a simple ensemble mechanism, resulting in the state-of-the-art performance.

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IJCAI Conference 2017 Conference Paper

Effective Deep Memory Networks for Distant Supervised Relation Extraction

Xiaocheng Feng
Jiang Guo
Bing Qin
Ting Liu
Yongjie Liu

Distant supervised relation extraction (RE) has been an effective way of finding novel relational facts from text without labeled training data. Typically it can be formalized as a multi-instance multi-label problem. In this paper, we introduce a novel neural approach for distant supervised (RE) with specific focus on attention mechanisms. Unlike the feature-based logistic regression model and compositional neural models such as CNN, our approach includes two major attention-based memory components, which is capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair, as well as the intrinsic dependencies between relations. Such importance degree and dependency relationship are calculated with multiple computational layers, each of which is a neural attention model over an external memory. Experiment on real-world datasets shows that our approach performs significantly and consistently better than various baselines.

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JAIR Journal 2016 Journal Article

A Distributed Representation-Based Framework for Cross-Lingual Transfer Parsing

Jiang Guo
Wanxiang Che
David Yarowsky
Haifeng Wang
Ting Liu

This paper investigates the problem of cross-lingual transfer parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g., English). Existing model transfer approaches typically don't include lexical features, which are not transferable across languages. In this paper, we bridge the lexical feature gap by using distributed feature representations and their composition. We provide two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space. Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer. Furthermore, our framework is flexible enough to incorporate additional useful features such as cross-lingual word clusters. Our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages. It also significantly outperforms state-of-the-art delexicalized models augmented with projected cluster features on identical data. Finally, we demonstrate that our models can be further boosted with minimal supervision (e.g., 100 annotated sentences) from target languages, which is of great significance for practical usage.

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AAAI Conference 2016 Conference Paper

A Representation Learning Framework for Multi-Source Transfer Parsing

Jiang Guo
Wanxiang Che
David Yarowsky
Haifeng Wang
Ting Liu

Cross-lingual model transfer has been a promising approach for inducing dependency parsers for lowresource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target languagespeciﬁc syntactic structures are difﬁcult to be recovered. To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. Our framework allows multi-source transfer parsing using full lexical features straightforwardly. By evaluating on the Google universal dependency treebanks (v2. 0), our best models yield an absolute improvement of 6. 53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also signiﬁcantly outperform the state-of-the-art transfer system proposed most recently.

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IJCAI Conference 2016 Conference Paper

Exploring Segment Representations for Neural Segmentation Models

Yijia Liu
Wanxiang Che
Jiang Guo
Bing Qin
Ting Liu

Many natural language processing (NLP) tasks can be generalized into segmentation problem. In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks. Our model represents a segment both by composing the input units and embedding the entire segment. We thoroughly study different composition functions and different segment embeddings. We conduct extensive experiments on two typical segmentation tasks: named entity recognition (NER) and Chinese word segmentation (CWS). Experimental results show that our neural semi-CRF model benefits from representing the entire segment and achieves the state-of-the-art performance on CWS benchmark dataset and competitive results on the CoNLL03 NER dataset.

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