# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 1 Introduction  
In recent years, thanks to the broad application of natural language processing (NLP) technology, financial information processing capabilities have been unprecedentedly improved. For example, studies have explored the effects of financial events on stock price predictions [1; 2], used firm reports to predict corporate performance [3], performed financial text sentiment analysis [4], and identified and extracted financial concepts (financial named entities, FNE) [5]. Notably, firm research reports have always been an important source of financial information. This information can be used to analyze the recent situation of a company from a professional perspective, make predictive assessment regarding the economic patterns and trends of the company in upcoming years, and provide professional investment advice. Previous studies [6] have also noted that compared to individual subjective texts, such as stocks and forums, the firm research reports are more realistic, and can provide a reliable source for financial text sentiment calculations and financial early warning decisions. In 1980, the American Stock Exchange required all listed companies to include a Management Discussion and Analysis (MD&A) section in their annual reports. MD&A focuses on the disclosure of forward-looking statements (FLSs) that may have a significant impact on the company. An FLS is an assessment and expectation of the future development trends and prospects of the company, and such information is of great significance to venture investors and other stakeholders. In recent years, many scholars have analyzed and thoroughly explored FLSs. For example, [PERSON] [7] used a naive Bayes classifier to explore the correlation between the information contained in FLSs in annual reports and economic factors. In article, our objective is to achieve the fine-scale recognition of forward-looking sentences in research reports; for example, \"[[[[[[[[[[[[[[[[]]]]]]]]]]]\" (The net profit contributed by the drug substance business will increase significantly). The elementtriple!Entity, Attribute, Attribute value? corresponds to!Entity? (drug substance business),? (net profit),!Entity? (significant increase)? in the sentence. This basic task can be applied in high-level applications such as sentiment analysis, investment decision making, and stock forecasting. Difficulties encountered in our work mainly include the following points. First, unlike other fields elements, finance is a relatively open field. Entities or attributes are very broad and difficult to identify. Second, new entities are constantly emerging. It is difficult to develop simple template rules that apply to all situations. Third, in the financial corpus, attributes, and attribute value elements appear mainly in the form of compound words or clauses. Therefore, the integrity of the recognition of the elements must also be considered. Deep learning can be used to overcome the two problems of open fields and new entities. In particular, deep learning is a data-driven approach that automatically identifies elements and builds models based on learning appropriate to the field. However, natural language itself is a highly symbolic and complex discrete rule, it is a collection of conventions and domain knowledge associated with the process of human evolution. In a purely data-driven approach, the language may lose its original meaning. Therefore, we combine deep learning with linguistic features and propose the LSTM-CRF model with the integrity algorithm, mainly to improve the recognition effect by correcting the boundaries of LSTM-CRF model annotations. This method combines the advantages of data-driven methods and dependency grammar to improve the accuracy and recall of elements. The structure of this paper is as follows. Section 2 introduces the status of element recognition research. Section 3 describes forward-looking element recognition based on the LSTM-CRF model with the integrity algorithm. Section 4 details the experimental steps and the analysis of the experimental results.