Recent years have seen a rapidly growing number of online reviews of products. As a result, it is often not possible for customers to go through each review before making purchase decisions. One way to address this problem is to build a system for automatically addressing the helpfulness of reviews and present only those reviews that are determined to be helpful by the system to an end user. The vast majority of existing approaches to the task of review helpfulness prediction are based on hand-crafted features, thus making system performance heavily dependent on the quality of these features. In light of this weakness, we propose a new model of review helpfulness prediction using a combination of Convolutional Neural Network (CNN) and TransE wherein hand-crafted features can also be incorporated to improve the output. Specifically, CNN enables us to learn the semantic information from a review and TransE is used to capture the relationship between different entities mentioned in the review. Experiments on the Amazon product review datasets demonstrate that our approach significantly outperforms the state of the art.

Out-of-Distribution (OOD) detection is a cornerstone for the safe deployment of AI systems in the open world. However, existing methods treat OOD detection as a binary classification problem, a cognitive flattening that fails to distinguish between semantically close (Near-OOD) and distant (Far-OOD) unknown risks. This limitation poses a significant safety bottleneck in applications requiring fine-grained risk stratification. To address this, we propose a paradigm shift from a conventional probabilistic view to a principled information-theoretic framework. We formalize the core task as quantifying the Semantic Surprise of a new sample and introduce a novel ternary classification challenge: In-Distribution (ID) vs. Near-OOD vs. Far-OOD. The theoretical foundation of our work is the concept of Low-Entropy Semantic Manifolds, which are explicitly structured to reflect the data's intrinsic semantic hierarchy. To construct these manifolds, we design a Hierarchical Prototypical Network. We then introduce the Semantic Surprise Vector (SSV), a universal probe that decomposes a sample's total surprise into three complementary and interpretable dimensions: conformity, novelty, and ambiguity. To evaluate performance on this new task, we propose the Normalized Semantic Risk (nSR), a cost-sensitive metric. Experiments demonstrate that our framework not only establishes a new state-of-the-art (sota) on the challenging ternary task, but its robust representations also achieve top results on conventional binary benchmarks, reducing the False Positive Rate by over 60% on datasets like LSUN.