Phrase mining is a fundamental task for text analysis and has various downstream applications such as named entity recognition, topic modeling, and relation extraction. In this paper, we focus on mining high-quality phrases from domain-specific corpora with special consideration of infrequent ones. Previous methods might miss infrequent high-quality phrases in the candidate selection stage. And these methods rely on explicit features to mine phrases while rarely considering the implicit features. In addition, completeness is rarely explicitly considered in the evaluation of a high-quality phrase. In this paper, we propose a novel approach that exploits a sequence labeling model to capture infrequent phrases. And we employ implicit semantic features and contextual POS tag statistics to measure meaningfulness and completeness, respectively. Experiments over four real-world corpora demonstrate that our method achieves significant improvements over previous state-of-the-art methods across different domains and languages.

Open attribute value extraction for emerging entities is an important but challenging task.A lot of previous works formulate the problem as a question-answering (QA) task.While the collections of articles from web corpus provide updated information about the emerging entities, the retrieved texts can be noisy, irrelevant, thus leading to inaccurate answers.Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy.Knowledge graph (KG), which contains rich, well organized information about entities, provides a good resource to address the challenge.In this work, we propose a knowledge-guided reinforcement learning (RL) framework for open attribute value extraction.Informed by relevant knowledge in KG, we trained a deep Q-network to sequentially compare extracted answers to improve extraction accuracy.The proposed framework is applicable to different information extraction system.Our experimental results show that our method outperforms the baselines by 16.5 -27.8%.

Commonsense knowledge acquisition is one of the fundamental issues in the implementation of human-level AI. However, commonsense is difficult to obtain, because it is a human consensus and rarely explicitly appears in texts or other data. In this paper, we focus on the automatic acquisition of a typical kind of implicit verb-oriented commonsense knowledge (e.g., "person eats food"), which is the concept level knowledge of verb phrases. For this purpose, we propose a knowledge-driven approach to mine verb-oriented commonsense knowledge from verb phrases with the help of taxonomy. First, we design an entropy-based filter to cope with noisy input verb phrases. Then, we propose a joint model based on minimum description length and a neural language model to generate verb-oriented common-sense knowledge. We conduct extensive experiments to show that our solution is more effective to mine verb-oriented commonsense knowledge than competitors, and finally, we harvest 18K verb-oriented commonsense knowledge.

Knowledge bases are playing an increasingly important role in many real-world applications. However, most of these knowledge bases tend to be outdated, which limits the utility of these knowledge bases. In this paper, we investigate how to keep the freshness of the knowledge base by synchronizing it with its data source (usually encyclopedia websites). A direct solution is revisiting the whole encyclopedia periodically and rerun the entire pipeline of the construction of knowledge base like most existing methods. However, this solution is wasteful and incurs massive overload of the network, which limits the update frequency and leads to knowledge obsolescence. To overcome the weakness, we propose a set of synchronization principles upon which we build an Update System for knowledge Base (USB) with an update frequency predictor of entities as the core component. We also design a set of effective features and realize the predictor. We conduct extensive experiments to justify the effectiveness of the proposed system, model, as well as the underlying principles. Finally, we deploy USB on a Chinese knowledge base to improve its freshness.