Context Modeling for Ranking and Tagging Bursty Features in Text Streams

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. ? 2010 ACM.EI

TWO-STAGE MODEL SELECTION WITH PARAMETERS WEIGHTED HIDDEN MARKOV MODELS AND LIKELIHOOD RATIO FOR PART-OF-SPEECH TAGGING

Abstract: In many natural language processing applications two or more models usually have to be involved for accuracy. But it is difficult for minor models, such as "backoff" taggers in part-of-speech tagging, to cooperate smoothly with the major probabilistic model. We introduce a two-stage approach for model selection between hidden Markov models and other minor models. In the first stage, the major model is extended to give a set of candidates for model selection. Parameters weighted hidden Markov model is presented using weighted ratio to create the candidate set. In the second stage, heuristic rules and features are used as evaluation functions to give extra scores to candidates in the set. Such scores are calculated using a diagnostic likelihood ratio test based on sensitivity and specificity criteria. The selection procedure can be fulfilled using swarm optimization technique. Experiment results on public tagging data sets show the applicability of the proposed approach

Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks

Accurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained.Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.0

A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses the customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, we find that such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendation into language matching problem by taking product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.Comment: 6 pages, 3 figures, 5 tables, accepted in 21st IEEE International Conference on Machine Learning and Application

Deletion of the meq gene significantly decreases immunosuppression in chickens caused by pathogenic marek's disease virus

<p>Abstract</p> <p>Background</p> <p>Marek's disease virus (MDV) causes an acute lymphoproliferative disease in chickens, resulting in immunosuppression, which is considered to be an integral aspect of the pathogenesis of Marek's disease (MD). A recent study showed that deletion of the Meq gene resulted in loss of transformation of T-cells in chickens and a Meq-null virus, rMd5ΔMeq, could provide protection superior to CVI988/Rispens.</p> <p>Results</p> <p>In the present study, to investigate whether the Meq-null virus could be a safe vaccine candidate, we constructed a Meq deletion strain, GX0101ΔMeq, by deleting both copies of the Meq gene from a pathogenic MDV, GX0101 strain, which was isolated in China. Pathogenesis experiments showed that the GX0101ΔMeq virus was fully attenuated in specific pathogen-free chickens because none of the infected chickens developed Marek's disease-associated lymphomas. The study also evaluated the effects of GX0101ΔMeq on the immune system in chickens after infection with GX0101ΔMeq virus. Immune system variables, including relative lymphoid organ weight, blood lymphocytes and antibody production following vaccination against AIV and NDV were used to assess the immune status of chickens. Experimental infection with GX0101ΔMeq showed that deletion of the Meq gene significantly decreased immunosuppression in chickens caused by pathogenic MDV.</p> <p>Conclusion</p> <p>These findings suggested that the Meq gene played an important role not only in tumor formation but also in inducing immunosuppressive effects in MDV-infected chickens.</p

Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue

Recent advances in Large Language Models (LLMs) have achieved remarkable breakthroughs in understanding and responding to user intents. However, their performance lag behind general use cases in some expertise domains, such as Chinese medicine. Existing efforts to incorporate Chinese medicine into LLMs rely on Supervised Fine-Tuning (SFT) with single-turn and distilled dialogue data. These models lack the ability for doctor-like proactive inquiry and multi-turn comprehension and cannot always align responses with safety and professionalism experts. In this work, we introduce Zhongjing, the first Chinese medical LLaMA-based LLM that implements an entire training pipeline from pre-training to reinforcement learning with human feedback (RLHF). Additionally, we introduce a Chinese multi-turn medical dialogue dataset of 70,000 authentic doctor-patient dialogues, CMtMedQA, which significantly enhances the model's capability for complex dialogue and proactive inquiry initiation. We define a refined annotation rule and evaluation criteria given the biomedical domain's unique characteristics. Results show that our model outperforms baselines in various capacities and matches the performance of ChatGPT in a few abilities, despite having 50x training data with previous best model and 100x parameters with ChatGPT. RLHF further improves the model's instruction-following ability and safety.We also release our code, datasets and model for further research

Beyond linguistic cues: fine-grained conversational emotion recognition via belief-desire modelling

Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. Although previous studies have made significant progress, accurate recognition and interpretation of similar fine-grained emotion properly accounting for individual variability remains a challenge. One particular under-explored area is the role of individual beliefs and desires in modelling emotion. Inspired by the Belief-Desire Theory of Emotion, we propose a novel method for conversational emotion recognition that incorporates both belief and desire to accurately identify emotions. We extract emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations. By applying message passing between nodes, our graph effectively models the utterance context, speaker's global state, and the interaction between emotional beliefs, desires, and utterances. We evaluate our model's performance by conducting extensive experiments on four popular ERC datasets and comparing it with multiple state-of-the-art models. The experimental results demonstrate the superiority of our proposed model and validate the effectiveness of each module in the model. © 2024 ELRA Language Resource Association: CC BY-NC 4.0