Entity Synonym Discovery via Multipiece Bilateral Context Matching

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization. Existing works either only utilize entity features, or rely on structured annotations from a single piece of context where the entity is mentioned. To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness. As one of the key components in synonym discovery, we introduce a neural network model SYNONYMNET to determine whether or not two given entities are synonym with each other. Instead of using entities features, SYNONYMNET makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema. Experimental results demonstrate that the proposed model is able to detect synonym sets that are not observed during training on both generic and domain-specific datasets: Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve and 3.19% in terms of Mean Average Precision compared to the best baseline method.Comment: In IJCAI 2020 as a long paper. Code and data are available at https://github.com/czhang99/SynonymNe

Multi-Grained Named Entity Recognition

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.Comment: In ACL 2019 as a long pape

A Numerical Simulation Approach for Superheated Steam Flow during Multipoint Steam Injection in Horizontal Well

Superheated steam flow during multipoint steam injection technology has a good effect on improving the steam absorption profile of heavy oil thermal recovery wells, enhancing the production degree of horizontal section of thermal recovery wells, and enhancing oil recovery. Based on the structure of multipoint steam injection horizontal string, considering the characteristics of variable mass flow, pressure drop of steam-liquid two-phase flow, and throttling pressure difference of steam injection valve in the process of steam injection, this paper establishes the calculation model of various parameters of multipoint steam injection horizontal wellbore and calculates the distribution of steam injection rate, temperature, pressure gradient, and dryness along the section of multipoint steam injection in horizontal wellbore. The results show that the temperature and pressure decrease gradually from heel to toe, and the steam dryness decreases gradually. Considering the influence of throttle pressure difference of steam injection valve and pressure drop of gas-liquid two-phase flow in the wellbore, the traditional calculation model of steam injection thermodynamic parameters is optimized, and the optimization of wellbore structure and steam injection parameters is an effective method to achieve uniform steam injection in horizontal wells. The steam injection uniformity of horizontal wells can be effectively improved by adjusting the steam injection valve spacing and steam injection parameters. When the steam injection volume is 200 m3/d and the steam injection valve spacing is 20 m, a more stable steam injection effect can be obtained. The findings of this study can help for better understanding of improving the uniformity of steam injection and enhancing the recovery factor

Highly Efficient Ag3PO4/g-C3N4 Z-Scheme Photocatalyst for Its Enhanced Photocatalytic Performance in Degradation of Rhodamine B and Phenol

Ag3PO4/g-C3N4 heterojunctions, with different g-C3N4 dosages, were synthesized using an in situ deposition method, and the photocatalytic performance of g-C3N4/Ag3PO4 heterojunctions was studied under simulated sunlight conditions. The results revealed that Ag3PO4/g-C3N4 exhibited excellent photocatalytic degradation activity for rhodamine B (Rh B) and phenol under the same light conditions. When the dosage of g-C3N4 was 30%, the degradation rate of Rh B at 9 min and phenol at 30 min was found to be 99.4% and 97.3%, respectively. After five cycles of the degradation experiment for Rh B, g-C3N4/Ag3PO4 still demonstrated stable photodegradation characteristics. The significant improvement in the photocatalytic activity and stability of g-C3N4/Ag3PO4 was attributed to the rapid charge separation between g-C3N4 and Ag3PO4 during the Z-scheme charge transfer and recombination process