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The role of HG in the analysis of temporal iteration and interaural correlation
A survey on opinion summarization technique s for social media
The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Selectional Preferences Based on Distributional Semantic Model
In this paper, we propose a approach based on distributional semantic model to the selectional preference in the verb & dobj (direct object) relationship. The distributional representations of words are employed as the semantic feature by using the Word2Vec algorithm. The machine learning method is used to build the discrimination model. Experimental results show that the proposed approach is effective to discriminate the compatibility of the object words and the performance could be improved by increasing the number of training data. By comparing the previous method, the proposed method obtain the promising results with obvious improvement. Moreover, the results demonstrate that the semantics is an universal, effective and stable feature in this task, which is consistent with our awareness of using words
Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation
News summary generation is an important task in the field of intelligence
analysis, which can provide accurate and comprehensive information to help
people better understand and respond to complex real-world events. However,
traditional news summary generation methods face some challenges, which are
limited by the model itself and the amount of training data, as well as the
influence of text noise, making it difficult to generate reliable information
accurately. In this paper, we propose a new paradigm for news summary
generation using LLM with powerful natural language understanding and
generative capabilities. We use LLM to extract multiple structured event
patterns from the events contained in news paragraphs, evolve the event pattern
population with genetic algorithm, and select the most adaptive event pattern
to input into the LLM to generate news summaries. A News Summary Generator
(NSG) is designed to select and evolve the event pattern populations and
generate news summaries. The experimental results show that the news summary
generator is able to generate accurate and reliable news summaries with some
generalization ability.Comment: 12 pages, 2 figure
Flexible and Creative Chinese Poetry Generation Using Neural Memory
It has been shown that Chinese poems can be successfully generated by
sequence-to-sequence neural models, particularly with the attention mechanism.
A potential problem of this approach, however, is that neural models can only
learn abstract rules, while poem generation is a highly creative process that
involves not only rules but also innovations for which pure statistical models
are not appropriate in principle. This work proposes a memory-augmented neural
model for Chinese poem generation, where the neural model and the augmented
memory work together to balance the requirements of linguistic accordance and
aesthetic innovation, leading to innovative generations that are still
rule-compliant. In addition, it is found that the memory mechanism provides
interesting flexibility that can be used to generate poems with different
styles
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