The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query.Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs or ads.However, the one-to-few mapping between numeric IDs and docs, along with the time-consuming content extraction, leads to semantic inefficiency and limits the scalability of existing methods on large-scale corpora.In this paper, we propose the Realtime Ad REtrieval (RARE) framework, which leverages LLM-generated text called Commercial Intentions (CIs) as an intermediate semantic representation to directly retrieve ads for queries in real-time.These CIs are generated by a customized LLM injected with commercial knowledge, enhancing its domain relevance.Each CI corresponds to multiple ads, yielding a lightweight and scalable set of CIs.RARE has been implemented in a real-world online system, handling daily search volumes in billions.The online implementation has yielded significant benefits: a 5.04% increase in consumption, a 6.37% increase in gross merchandise volume (GMV), a 1.28% enhancement in click-through rate (CTR) and a 5.29% increase in shallow conversions.Extensive offline experiments show RARE's superiority over ten competitive baselines in four major categories.

Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which require many complex, hand-designed features. In addition, the words encoded by one-hot are unable to represent semantic information. Therefore, we utilize dependency-based embeddings to represent words semantically and syntactically. Then, we propose a parallel multi-pooling convolutional neural network (PMCNN) model to capture the compositional semantic features of sentences. Furthermore, we employ a rectified linear unit, which creates sparse representations with true zeros, and which is adapted to the biomedical event extraction, as a nonlinear function in PMCNN architecture. The experimental results from MLEE dataset show that our approach achieves an F1 score of 80.27 percent in trigger identification and an F1 score of 59.65 percent in biomedical event extraction, which performs better than other state-of-the-art methods.

Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent the semantic information. Joint inference with dual decomposition and novel word embeddings are adopted to address the two problems, respectively, in this work. Word embeddings are learnt from large scale unlabeled texts and integrated as an unsupervised feature into other rich features based on dependency parse graphs to detect triggers and arguments. The proposed system consists of four components: trigger detector, argument detector, jointly inference with dual decomposition, and rule-based semantic post-processing, and outperforms the state-of-the-art systems. On the development set of BioNLP'09, the F-score is 59.77 percent on the primary task, which is 0.96 percent higher than the best system. On the test set of BioNLP'11, the F-score is 56.09 and 0.89 percent higher than the best published result that do not adopt additional techniques. On the test set of BioNLP'13, the F-score reaches 53.19 percent which is 2.22 percent higher than the best result.