Word embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the problem of how to effectively embed numerals, a special subset of words, is still underexplored. Existing word embedding methods do not learn numeral embeddings well because there are an infinite number of numerals and their individual appearances in training corpora are highly scarce. In this paper, we propose two novel numeral embedding methods that can handle the out-of-vocabulary (OOV) problem for numerals. We first induce a finite set of prototype numerals using either a selforganizing map or a Gaussian mixture model. We then represent the embedding of a numeral as a weighted average of the prototype number embeddings. Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training. We evaluated our methods and showed its effectiveness on four intrinsic and extrinsic tasks: word similarity, embedding numeracy, numeral prediction, and sequence labeling.

Event extraction is a classic task in natural language processing with wide use in handling large amount of yet rapidly growing financial, legal, medical, and government documents which often contain multiple events with their elements scattered and mixed across the documents, making the problem much more difficult. Though the underlying relations between event elements to be extracted provide helpful contextual information, they are somehow overlooked in prior studies.We showcase the enhancement to this task brought by utilizing the knowledge graph that captures entity relations and their attributes. We propose a first event extraction framework that embeds a knowledge graph through a Graph Neural Network and integrates the embedding with regular features, all at documentlevel. Specifically, for extracting events from Chinese financial announcements, our method outperforms the state-of-the-art method by 5.3% in F1-score.

Various types of relationships among participants exist in the foreign exchange market, such as transaction, credit relations inside the market and subcompany, investing and shareholding relations outside the market. In the market with complex relationships, an incident happened in one institution may have “butterfly effect”. To monitor systemic financial risks, we integrate various types of data and establish a Knowledge Graph describing the relationships between market participants. Based on the Knowledge Graph, we design a framework to measure the impact of various events on the entire market by connecting the internal market relationships to external market related incidents. By using the BiLSTM-CRF model, we extract the relevant entities in the market news with high accuracy and measure the impact on the market with graph related indicators such as closeness and betweenness centralities. In addition, to use external unstructured data, we use a document-level event extraction method to analyse these unstructured datasets and extract new events and relationships from them to dynamically update the Knowledge Graph.

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