RESUMEN
End-to-End aspect-based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and identify their sentiment polarities. Although previous research has demonstrated that syntax knowledge can be beneficial for E2E-ABSA, standard syntax dependency parsing struggles to capture the block-level relation between aspect and opinion terms, which hinders the role of syntax in E2E-ABSA. To address this issue, this paper proposes a block-level dependency syntax parsing (BDEP) based model to enhance the performance of E2E-ABSA. BDEP is constructed by incorporating routine dependency syntax parsing and part-of-speech tagging, which enables the capture of block-level relations. Subsequently. the BDEP-guided interactive attention module (BDEP-IAM) is used to obtain the aspect-aware representation of each word. Finally the adaptive fusion module is leveraged to combine the semantic-syntactic representation to simultaneously extract the aspect term and identify aspect-orient sentiment polarity. The model is evaluated on five benchmark datasets, including Laptop14, Rest _ALL, Restaurant14, Restaurant15, and TWITTER, with F1 scores of 62.67%, 76.53%, 75.42%, 62.21%, and 58.03%, respectively. The results show that our model outperforms the other compared state-of-the-art (SOTA) methods on all datasets. Additionally, ablation experiments confirm the efficacy of BDEP and IAM in improving aspect-level sentiment analysis.
Asunto(s)
Semántica , Análisis de Sentimientos , Humanos , HablaRESUMEN
Although satellite radar altimetry was developed and optimized for open oceans, it has been used to monitor variations in the level of inland water-bodies such as lakes and rivers. Here, for the first time, we have further used the altimetry-derived variation of water level for estimating the fluctuation of water storage as an addition to the present in situ water storage estimation systems to be used in remote areas and in emergency situation such as in the events flooding monitoring and for studying the effect of climate change. Lake Dongting, the second largest lake in China, influenced frequently by flooding, was, therefore, chosen to demonstrate the potential of the technique. By using the concept of an "assumed reference point", we converted Topex/Poseidon satellite altimetry data on water level variations in Lake Dongting to "water level" data. The "water level" time-series data and in situ water storage were used to establish a rating curve. From the rating curve, we converted data on "water level" derived from seven years (1993-1999) of Topex/Poseidon data to actual water storage in Lake Dongting. The result reveals that the seasonal and annual fluctuations of water storage occurred during the 1990s with a more frequent flooding at the late 1990s' especially the flooding in whole catchment level in 1998 and 1999. The study supports the usefulness of satellite altimetry for dense and continuous monitoring of the temporal variations in water dynamic in moderate to large lakes.