The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training and testing data are from the same domain. However, these methods show a drastic decrease in performance when applied to cross-domain settings where the domain of the testing data differs from that of the training data. To address this lack of extensibility and robustness, we propose a novel approach for automatically constructing domain-specific knowledge graphs that contain information relevant to the identification of aspect terms. We introduce a methodology for injecting information from these knowledge graphs into Transformer models, including two alternative mechanisms for knowledge insertion: via query enrichment and via manipulation of attention patterns. We demonstrate state-of-the-art performance on benchmark datasets for cross-domain aspect term extraction using our approach and investigate how the amount of external knowledge available to the Transformer impacts model performance.

Creating ICs in the nanometer age is a high-stakes race that few companies can afford to compete in - and even fewer can win. Hear how senior technologists from the world's top technology companies are striving to improve their chances of success. Will leakage constraints force power-sensitive applications to stay with older technologies, or will there be a bifurcation to a new process technology? Will ballooning capital equipment expenses delay new capacity or price out design rules for mainstream applications? Will silicon-on-insulator and new device structures like FinFETs force rethinking of design, modeling and simulation methodologies? And which EDA technologies, delivered when, will be critical for victory? These senior technologists, from some of the biggest companies in the high tech industry, will discuss and debate how they think the overall industry will successfully transition to the nanometer age. Specific examples from the technologists' broad exposure to industry trends and competitors will help illustrate their forecasts and predictions.