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Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization , take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction(More)
Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent con-volutional neural network for text classification without human-designed features. In(More)
Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper , we propose a more fine-grained model named TransD, which is an(More)
With the increasing development of Web 2.0, such as social media and online businesses, the need for perception of opinions, attitudes, and emotions grows rapidly. Sentiment analysis, the topic studying such subjective feelings expressed in text, has attracted significant attention from both the research community and industry. Although we have known(More)
Mining opinion targets and opinion words from online reviews are important tasks for fine-grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment(More)
This paper proposes a novel approach to extract opinion targets based on word-based translation model (WTM). At first, we apply WTM in a monolingual scenario to mine the associations between opinion targets and opinion words. Then, a graph-based algorithm is exploited to extract opinion targets, where candidate opinion relevance estimated from the mined(More)
Mining opinion targets is a fundamental and important task for opinion mining from online reviews. To this end, there are usually two kinds of methods: syntax based and alignment based methods. Syntax based methods usually exploited syntactic patterns to extract opinion targets, which were however prone to suffer from parsing errors when dealing with online(More)
Extracting opinion targets and opinion words from online reviews are two fundamental tasks in opinion mining. This paper proposes a novel approach to collectively extract them with graph co-ranking. First, compared to previous methods which solely employed opinion relations among words, our method constructs a heterogeneous graph to model two types of(More)
Distantly supervised relation extraction, which can automatically generate training data by aligning facts in the existing knowledge bases to text, has gained much attention. Previous work used conjunction features with coarse entity types consisting of only four types to train their models. Entity types are important indicators for a specific relation, for(More)
This paper proposes a novel two-stage method for mining opinion words and opinion targets. In the first stage, we propose a Sentiment Graph Walking algorithm , which naturally incorporates syntactic patterns in a Sentiment Graph to extract opinion word/target candidates. Then random walking is employed to estimate confidence of candidates, which improves(More)