ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction

A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines

Current status and behavior modeling on household solid-waste separation: a case study in Da Nang city, Vietnam

This study focused on household solid-waste recycling in Da Nang city, Vietnam to assess the existing separation behavior and clarify the factors influencing the separation behavior. The authors conducted a questionnaire survey for 150 households in 6 urban districts, which consisted of household attributes, separation behavior, and the household's attitude on recycling and the environment. The waste separation rates were determined for leftover food and 13 recyclable items and the recyclable disposal habit was also assessed. The separation rate of leftover food was 77.3%. Among 13 surveyed recyclable items, plastic bottles and metal cans were two popular items with higher separation rate (72.5% and 63.8%, respectively). To identify the conscious structure and determinants of separation behavior, the authors developed a predictive model on the separation behavior of leftover food and recyclables by logistic and multiple linear regression analyses. The positive factors included behavior intention, sympathy for the collector, incentive brought by recycling, goal intention, internal norm, and perception of responsibility and seriousness. The negative factor was evaluation of trouble. The authors also analyzed the differences in separation rates among attributes. Based on the significant influence factors and attributes, the authors suggested how to promote separation behavior

A Text-based Approach For Link Prediction on Wikipedia Articles

This paper present our work in the DSAA 2023 Challenge about Link Prediction for Wikipedia Articles. We use traditional machine learning models with POS tags (part-of-speech tags) features extracted from text to train the classification model for predicting whether two nodes has the link. Then, we use these tags to test on various machine learning models. We obtained the results by F1 score at 0.99999 and got 7th place in the competition. Our source code is publicly available at this link: https://github.com/Tam1032/DSAA2023-Challenge-Link-prediction-DS-UIT_SATComment: Accepted by DSAA 2023 Conference in the DSAA Student Competition Sectio

Detecting Machine-Translated Text using Back Translation

Machine-translated text plays a crucial role in the communication of people using different languages. However, adversaries can use such text for malicious purposes such as plagiarism and fake review. The existing methods detected a machine-translated text only using the text's intrinsic content, but they are unsuitable for classifying the machine-translated and human-written texts with the same meanings. We have proposed a method to extract features used to distinguish machine/human text based on the similarity between the intrinsic text and its back-translation. The evaluation of detecting translated sentences with French shows that our method achieves 75.0% of both accuracy and F-score. It outperforms the existing methods whose the best accuracy is 62.8% and the F-score is 62.7%. The proposed method even detects more efficiently the back-translated text with 83.4% of accuracy, which is higher than 66.7% of the best previous accuracy. We also achieve similar results not only with F-score but also with similar experiments related to Japanese. Moreover, we prove that our detector can recognize both machine-translated and machine-back-translated texts without the language information which is used to generate these machine texts. It demonstrates the persistence of our method in various applications in both low- and rich-resource languages.Comment: INLG 2019, 9 page

Multi-correlation between nematode communities and environmental variables in mangrove-shrimp ponds, Ca Mau Province, Southern Vietnam

Multi-correlation between bio-indices of nematode communities and ecological parameters in mangrove-shrimp farming ponds in Tam Giang commune, Nam Can District, Ca Mau Province, Vietnam were investigated. In which, diversities of nematode communities and several environmental variables in eight ponds were considered to process. Our findings underlined the high diversity of nematode communities in mangrove-shrimp farming ponds compared to other mangrove habitats. Nematode diversities provided more oppotunity in natural food for shrimp. Single correlation analyses showed that the species richness index correlated significantly to three variables (salinity, total organic carbon, and total nitrogen), the Margalef diversity index correlated to two variables (salinity, total organic carbon), and the expected number of species for 50 individuals index correlated with one variable (salinity). Results of multi-correlation analyses between the nematode bio-indices and the environmental variables were completely different from those of single-correlation analyses. In multi-correlation analyses, the species richness and the Margalef diversity index correlated to two variables (salinity, total organic carbon), Pielou’s evenness index and Hill indices correlated with dissolved oxygen, also the Hurlbert index correlated to total organic carbon. Hence, it is necessary to pay attention to the impact of complex interactions between the multi-environmental variables and nematode communities. This research aims to explain the differences between single- and multi-correlation for evaluation of the effects of environmental factors on nematodes as well as aquatic organisms.