The end-to-end aspect-based social comment sentiment analysis (E2E-ABSA) task aims to discover human’s fine-grained sentimental polarity, which can be refined to determine the attitude in response to an object revealed in a social user’s textual description. The E2E-ABSA problem includes two sub-tasks, i.e., opinion target extraction and target sentiment identification. However, most previous methods always tend to model these two tasks independently, which inevitably hinders the overall practical performance. This paper investigates the critical collaborative signals between these two sub-tasks and thus proposes a novel cascade social comment sentiment analysis model for jointly tackling the E2E-ABSA problem, namely CasNSA. Instead of treating the opinion target extraction and target sentiment identification as discrete procedures in previous works, our new framework takes the contextualized target semantic encoding into consideration to yield better sentimental polarity judgment. Additionally, extensive empirical results show that the proposed approach effectively achieves a 68.13% F1-score on SemEval-2014, 62.34% F1-Score on SemEval-2015, 56.40% F1-Score on SemEval-2016, and 50.05% F1-score on a Twitter dataset, which is higher than the existing approaches. Ablated experiments demonstrate that the CasNSA model substantially outperforms state-of-the-art methods, even when using fixed words embedding rather than pre-trained BERT fine tuning. Moreover, in-depth performance analysis on the social comment datasets further validates that our work gains superior performance and reliability effectively and efficiently in realistic scenarios.
Knowledge Base Question Answering (KBQA) aims to answer user questions from a knowledge base (KB) by identifying the reasoning relations between topic entity and answer. As a complex branch task of KBQA, multi-hop KGQA requires reasoning over multi-hop relational chains preserved in KG to arrive at the right answer. Despite the successes made in recent years, the existing works on answering multi-hop complex question face the following challenges: i) suffering from poor performances due to the neglect of explicit relational chain order and its relational types reflected in user questions; ii) failing to consider implicit relations between the topic entity and the answer implied in structured KG because of limited neighborhood size constraints in subgraph retrieval-based algorithms. To address these issues in multi-hop KGQA, we propose a novel model in this paper, namely Relational Chain-based Embedded KGQA (Rce-KGQA), which simultaneously utilizes the explicit relational chain described in natural language questions and the implicit relational chain stored in structured KG. Our extensive empirical study on two open-domain benchmarks proves that our method significantly outperforms the state-of-the-art counterparts like GraftNet, PullNet and EmbedKGQA. Comprehensive ablation experiments also verify the effectiveness of our method for multihop KGQA tasks. We have made our model's source code available at Github: https://github.com/albert-jin/Rce-KGQA.
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