Named entity recognition (NER) is a fundamental task for information extraction applications. NER is challenging because of semantic ambiguities in academic literature, especially for non-Latin languages. Besides word semantic information, recognizing Chinese named entities needs to consider word boundary information, as words contained in Chinese texts are not separated with spaces. Leveraging word boundary information could help to determine entity boundaries and thus improve entity recognition performance. In this article, we propose to combine word boundary information and semantic information for named entity recognition based on multi-task adversarial learning. Specifically, we learn commonly shared boundary information of entities from multiple kinds of tasks, including Chinese word segmentation (CWS), part-of-speech (POS) tagging, and entity recognition, with adversarial learning. We learn task-specific semantic information of words from these tasks and combine the learned boundary information with the semantic information to improve entity recognition with multi-task learning. We then propose a compression method based on improved clustering to accelerate the proposed model. We conduct extensive experiments on four public benchmark datasets and two private datasets, compared with state-of-the-art baseline models, and the experimental results demonstrate that our model achieves considerable performance improvements on various evaluation datasets.

Named entity recognition (NER) isa preliminary task in natural language processing (NLP). Recognizing Chinese named entities from unstructured texts is challenging due to the lack of word boundaries. Even if performing Chinese Word Segmentation (CWS) could help to determine word boundaries, it is still difficult to determine which words should be clustered together for entity identification, since entities are often composed of multiple-segmented words. As dependency relationships between segmented words could help to determine entity boundaries, it is crucial to employ information related to syntactic dependency relationships to improve NER performance. In this paper, we propose a novel NER model to learn information about syntactic dependency graphs with graph neural networks, and merge learned information into the classic Bidirectional Long Short-Term Memory (BiLSTM) - Conditional Random Field (CRF) NER scheme. In addition, we extract various kinds of task-specific hidden information from multiple CWS and part-of-speech (POS) tagging tasks, to further improve the NER model. We finally leverage multiple self-attention components to integrate multiple kinds of extracted information for named entity identification. Experimental results on three public benchmark datasets show that our model outperforms the state-of-the-art baselines in most scenarios.

Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior. Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. In particular, transaction records are modeled by attention and 3D convolution mechanisms by integrating the corresponding information, including spatial and temporal behaviors. Attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. Afterward, we conduct extensive experiments on real-word fraud transaction dataset, the result shows that STAN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud post-analysis; the result demonstrates the effectiveness of our proposed method in both detecting suspicious transactions and mining fraud patterns.

Many institutions are devoted to providing investment advising services to stock investors to help them make sound investment decisions. Industry analysts at these institutions need to analyze huge amounts of financial news documents, and yield investment advising reports to the service subscribers. Automatic document classification is required to organize collected financial news documents into pre-defined fine-grained categories, before the document analysis tasks. It is challenging to implement accurate fine-grained classification over massive financial documents, because documents from close fine-grained categories are highly semantically similar, while existing classification methods may fail to differentiate the subtle differences for documents from close fine-grained categories. In this paper, we implement a document classification framework, named GraphSEAT, to classify financial documents for a leading financial information service provider in China. Specifically, we build a heterogeneous graph to model the global structure of our targeting financial documents, where documents and financial named entities are deemed as nodes, and a document is connected to a contained named entity with an edge, and we then train a graph convolutional network (GCN) with attention mechanisms, to learn an embedding representation containing domain information for a document. We also extract semantic information from a document's word sequence with a neural sequence encoder, and finally form an overall embedding representation for a document and make the prediction, via fusing the two learned representations of the document with attention mechanisms. We perform extensive experiments on our real-world financial news dataset and three public datasets, to evaluate the performance of the document classification framework, and the experimental results demonstrate that GraphSEAT outperforms all compared eight baseline models, especially on our dataset.

The share prices of listed companies in the stock trading market are prone to be influenced by various events. Performing event detection could help people to timely identify investment risks and opportunities accompanying these events. The financial events inherently present hierarchical structures, which could be represented as tree-structured schemes in real-life applications, and detecting events could be modeled as a hierarchical multi-label text classification problem, where an event is designated to a tree node with a sequence of hierarchical event category labels. Conventional hierarchical multi-label text classification methods usually ignore the hierarchical relationships existing in the event classification scheme, and treat the hierarchical labels associated with an event as uniform labels, where correct or wrong label predictions are assigned with equal rewards or penalties. In this paper, we propose a neural hierarchical multi-label text classification method, namely F-HMTC, for a financial application scenario with massive event category labels. F-HMTC learns the latent features based on bidirectional encoder representations from transformers, and directly maps them to hierarchical labels with a delicate hierarchy-based loss layer. We conduct extensive experiments on a private financial dataset with elaborately-annotated labels, and F-HMTC consistently outperforms state-of-art baselines by substantial margins. We will release both the source codes and dataset on the first author's repository.

Event representative learning aims to embed news events into continuous space vectors for capturing syntactic and semantic information from text corpus, which is benefit to event-driven quantitative investments. However, the financial market reaction of events is also influenced by the lead-lag effect, which is driven by internal relationships. Therefore, in this paper, we present a knowledge graph-based event embedding framework for quantitative investments. In particular, we first extract structured events from raw texts, and construct the knowledge graph with the mentioned entities and relations simultaneously. Then, we leverage a joint model to merge the knowledge graph information into the objective function of an event embedding learning model. The learned representations are fed as inputs of downstream quantitative trading methods. Extensive experiments on real-world dataset demonstrate the effectiveness of the event embeddings learned from financial news and knowledge graphs. We also deploy the framework for quantitative algorithm trading. The accumulated portfolio return contributed by our method significantly outperforms other baselines.

Commercial banks normally require Small and Medium Enterprises (SMEs) to provide their warranties when applying for a loan. If the borrower defaults, the guarantor is obligated to repay its loan. Such a guarantee system is designed to reduce delinquent risks, but may introduce a new dimension risk if more and more SMEs involve and subsequently form complex temporal networks. Monitoring the financial status of SMEs in these networks, and preventing or reducing systematic loan risk, is an area of great concern for both the regulatory commission and the banks. To allow possible actions to be taken in advance, this paper studies the problem of predicting repayment delinquency in the networked-guarantee loans. We propose a dynamic default prediction framework (DDPF), which preserves temporal network structures and loan behavior sequences in an end-to-end model. In particular, we design a gated recursive and attention mechanism to integrate both the loan behavior and network information. Then, we uncover risky warrant patterns by the learned weights, which effectively accelerate risk evaluation process. Finally, we conduct extensive experiments in a real-world loan risk control system to evaluate its performance, the results demonstrate the effectiveness of our proposed approach compared with state-of-the-art baselines.

Personalized flight recommendation is useful when a traveler wants to find the most suitable flight ticket efficiently. It is hard to learn latent factors behind personal choices directly from users' preferred flight tickets whose features change over time, which leads to the ineffectiveness of traditional recommendation approaches. Therefore, we propose a personalized flight ticket recommendation approach based on paired choice modeling. In this approach, an optimization problem is formulated to maximize a user's choice utility over flight tickets through a paired-choice analysis of users' historical orders. The experiments conducted on a real world dataset show that our approach outperforms other state-of-the-art methods.