While automated CT report generation (CTRG) systems promise to enhance clinical workflow efficiency, current solutions, even those based on advanced multi-modal large language models (MLLMs), face fundamental challenges in ensuring report quality and reliability. Through systematic analysis of representation dynamics in MLLM-based CTRG models, we discover that existing systems lack robust quality discrimination capabilities, manifesting in two critical limitations: representation entanglement between reports of varying quality levels in the feature space, and quality-insensitive generation due to conventional training paradigms focusing solely on ground-truth reports. To address these limitations, we propose CT-ClearView, a quality-aware cross-modal alignment framework consisting of two key innovations: (1) a systematic methodology for constructing clinically-relevant hard negative examples using GPT-4, which introduces subtle but significant clinical errors while maintaining report structure and plausibility, and (2) a contrastive learning framework that leverages these examples to effectively disentangle representations of varying quality reports and enhance the model's sensitivity to clinical details. Extensive experiments on CTRG-Chest-548K and CTRG-Brain-263K datasets demonstrate significant performance improvements in natural language generation (NLG) metrics compared to existing approaches (e.g., increases of 11% in BLEU-1 and 16% in both BLEU-4 and ROUGE-L, on the CTRG-Chest-548K datasets).

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we focus on the use of local conceptual word embeddings for enhance microblog retrieval effectiveness. In particular, we propose a novel k-Nearest Neighbor (kNN) based Query Expansion (QE) algorithm to generate words from local word embeddings to expand the original query, which leads to better understanding of the information need. Besides, in order to further satisfy users' real-time information need, we incorporate temporal evidences into the expansion algorithm, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on the official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.

In this paper, we introduce the task of resource citation classification for scientific literature using a context-based framework. This task is to analyze the purpose of citing an on-line resource in scientific text by modeling the role and function of each resource citation. It can be incorporated into resource indexing and recommendation systems to help better understand and classify on-line resources in scientific literature. We propose a new annotation scheme for this task and develop a dataset of 3,088 manually annotated resource citations. We adopt a neural-based model to build the classifiers and apply them on the large ARC dataset to examine the revolution of scientific resources from trends in their function over time.

Most short-text embedding models typically represent each short-text only using the literal meanings of the words, which makes these models indiscriminative for the ubiquitous polysemy. In order to enhance the semantic representation capability of the short-texts, we (i) propose a novel short-text conceptualization algorithm to assign the associated concepts for each short-text, and then (ii) introduce the conceptualization results into learning the conceptual short-text embeddings. Hence, this semantic representation is more expressive than some widely-used text representation models such as the latent topic model. Wherein, the short-text conceptualization algorithm used here is based on a novel co-ranking framework, enabling the signals (i.e., the words and the concepts) to fully interplay to derive the solid conceptualization for the short-texts. Afterwards, we further extend the conceptual short-text embedding models by utilizing an attention-based model that selects the relevant words within the context to make more efficient prediction. The experiments on the real-world datasets demonstrate that the proposed conceptual short-text embedding model and short-text conceptualization algorithm are more effective than the state-of-the-art methods.

We tackle the problem of improving microblog retrieval algorithms by proposing a Feedback Concept Model for query expansion. In particular, we expand the query using knowledge information derived from Probase so that the expanded one could better reflect users' search intent, which allows for microblog retrieval at a concept-level, rather than term-level. In the proposed feedback concept model: (i) we mine the concept information implicit in short-texts based on the external knowledge bases; (ii) with the relevant concepts associated with short-texts, a mixture model is generated to estimate a concept language model; (iii) finally, we utilize the concept language model for query expansion. Moreover, we incorporate temporal prior into the proposed query expansion method to satisfy real-time information need. Finally, we test the generalization power of the feedback concept model on the TREC Microblog corpora. The experimental results demonstrate that the proposed model outperforms the previous methods for microblog retrieval significantly.