Increasingly, more software services have been published onto the Internet,
making it a big challenge to recommend services in the process of a scientific
workflow composition. In this paper, a novel context-aware approach is proposed
to recommending next services in a workflow development process, through
learning service representation and service selection decision making behaviors
from workflow provenance. Inspired by natural language sentence generation, the
composition process of a scientific workflow is formalized as a step-wise
procedure within the context of the goal of workflow, and the problem of next
service recommendation is mapped to next word prediction. Historical service
dependencies are first extracted from scientific workflow provenance to build a
knowledge graph. Service sequences are then generated based on diverse
composition path generation strategies. Afterwards, the generated corpus of
composition paths are leveraged to study previous decision making strategies.
Such a trained goal-oriented next service prediction model will be used to
recommend top K candidate services during workflow composition process.
Extensive experiments on a real-word repository have demonstrated the
effectiveness of this approach.

提出了一种新颖的上下文感知方法，通过从工作流溯源中学习服务表示和服务选择决策行为，推荐下一个工作流开发过程中的服务。

科学工作流开发中的服务选择决策行为学习

Learning Service Selection Decision Making Behaviors During Scientific  Workflow Development

The issue of word sense ambiguity poses a significant challenge in natural
language processing due to the scarcity of annotated data to feed machine
learning models to face the challenge. Therefore, unsupervised word sense
disambiguation methods have been developed to overcome that challenge without
relying on annotated data. This research proposes a new context-aware approach
to unsupervised word sense disambiguation, which provides a flexible mechanism
for incorporating contextual information into the similarity measurement
process. We experiment with a popular benchmark dataset to evaluate the
proposed strategy and compare its performance with state-of-the-art
unsupervised word sense disambiguation techniques. The experimental results
indicate that our approach substantially enhances disambiguation accuracy and
surpasses the performance of several existing techniques. Our findings
underscore the significance of integrating contextual information in semantic
similarity measurements to manage word sense ambiguity in unsupervised
scenarios effectively.

本研究提出一种新的上下文感知的无监督词义消歧方法，通过将上下文信息纳入相似度量计算过程中，有效地管理文本中的词义模糊，从而提高了词义消歧的准确性并超越了现有的多种技术。

上下文感知的语义相似度测量用于无监督词义消歧

Context-Aware Semantic Similarity Measurement for Unsupervised Word  Sense Disambiguation

In translation, considering the document as a whole can help to resolve
ambiguities and inconsistencies. In this paper, we propose a cross-sentence
context-aware approach and investigate the influence of historical contextual
information on the performance of neural machine translation (NMT). First, this
history is summarized in a hierarchical way. We then integrate the historical
representation into NMT in two strategies: 1) a warm-start of encoder and
decoder states, and 2) an auxiliary context source for updating decoder states.
Experimental results on a large Chinese-English translation task show that our
approach significantly improves upon a strong attention-based NMT system by up
to +2.1 BLEU points.

本文提出了一种基于上下文的翻译方法，并探究了历史上下文信息对神经机器翻译性能的影响。实验结果表明，该方法相较于基于注意力机制的神经机器翻译系统，能够显著提高翻译质量 + 2.1 BLEU points。