Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management (2021)

Abstract: Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes. One of the most crucial tasks of BPM is discovering and modelling business processes from text documents. In this paper, we present our system that resolves an end-to-end problem consisting of 1) recognizing conditional sentences from technical documents, 2) finding boundaries to extract conditional and resultant clauses from each conditional sentence, and 3) categorizing resultant clause as Action or Consequence which later helps to generate new steps in our business process model automatically. We created a new dataset and three models to solve this problem. Our best model achieved very promising results of 83.82, 87.84, and 85.75 for Precision, Recall, and F1, respectively, for extracting Condition, Action, and Consequence clauses using Exact Match metric.

## introduction business processes which serve as basis for many (if not all) companies are usually stored as unstructured data, especially as text documents @xcite . they can reflect all organization tasks and activities in order to provide services. therefore, these documents could be converted into process models which allows to discover, analyze, redesign, monitor and control these business processes (m. dumas, 2018). the discipline which is responsible for management of this life cycle is known as business process management (bpm). today, this well known practice is very interdisciplinary and combines: organizational theory, management, and even computer science. one of the sub-areas of computer science is natural language processing (nlp) and due to large number of freely available nlp tools such as taggers, parsers, and the lexical database wordnet, nlp is applied in a multitude of domains and bmp is no exception. the application of nlp techniques could be used to extract useful information from textual documents to infer a process model, and also to extract information from the process model to facilitate visual process analysis @xcite . process modelling is a very complex and timeconsuming task. in this work we mainly focused on the analysis of extracted information from text documents in order to have more accurate process models. given a list of extracted sentences from technical documents, the contributions of our work consists of the following: 1) classifying if a sentence is a conditional sentence, 2) identifying and extracting the conditional and resultant clauses from a conditional sentence, and 3) categorizing the extracted resultants. section 2 introduces business context and application for our tasks. section 3 explains how we defined the tasks and created the new dataset techcond towards solving the tasks. section 4 presents our methods and models for the tasks. section 5 shows experiments, evaluation results, and the error analysis that identifies the limitations of our models. ## business context, application, and related works process discovery traditional process models based on interviews often provide only a limited or biased picture of the actual process. such highly abstract result can be interpreted in many ways. therefore, during the past decade process mining and visualization tools (i.e., celonis, uipath) were developed to improve process visibility by generating highly adaptable, highly maintainable and validated business process models. however, these tools focus only on analysis of results from structured data -process sequences from actual user desktops, or systems logs. it is well known that desktop procedures (dtps) and standard operation procedures (sop) are very common process documentation techniques @xcite . therefore, in order to see a broader picture of the process the information from textual source should be analyzed and included into process model. one of the tools which includes multiple sources is process discovery accelerator (pda). application pda is a business process mining and discovery tool currently available only for automation consultants of our company. it runs as a web application in production on the services essentials for automation platform of our company. pda has three main components: 1) standard operation procedure file analysis; 2) click stream file analysis, sequential structured data which represents actual users activities; and 3) system logs file analysis which is also structured data from applications (system, e-mail, chat). process discovery accelerator is able to discover processes using separate source components or all available at once, this procedure is called process harmonization. the final view of this procedure is single, correlated or uncorrelated processes from all available sources. the example of a single (only sop) simple process graph result in pda ui is shown in figure 1 . it illustrates how process flow looks like when conditional and resultant clauses are split into separate steps. the analysis of standard operation procedures is very challenging not only because it is based on unstructured data but also because it is very nonhomogeneous, domain specific and biased since it is highly dependent on a process description creator @xcite . these sop files usually contain complex step descriptions, i.e., "if something, then do this" ( the main objective of pda is to find automation opportunities by analyzing available data from standard operation procedures, click stream, and log files. without accurate analysis of sop files it is difficult to measure differences between different sources. our proposed solution for conditional splitting is crucial for the sop analysis, since it allows to see actual decision points and how complex a process is by detecting actions and/or conse-quences. related works the study (wenzina r., 2013) proposed heuristic, rule-based method to identify condition-action sentences. by using a set of linguistic patterns authors were able to split up sentences semantically into their clauses showing the condition and the consequence with recall of 75% and precision of 88%. note: these numbers are for specific patterns -for all activities recall is 56%. another rule-based work was presented by @xcite . this work is based on combinations of part of speech tags used as features. authors applied more statistical approach to automatically extract features, however, the recall value is very similar to @xcite . both works were tested using sentences from medical sop's. another good example of nlp and business process management combination is the work from @xcite . authors used latest nlp techniques (transformers) to classify textual information from word-level to sentence-level. although this work is most similar to our work it focuses more on automatic process graph extraction, which aims to extract multi-granularity information without manually defined procedural knowledge. ## task definition and new dataset in this section, we first analyzed the sampled data from our client, then we defined the task conditional sentence splitting, and created a new dataset techcond to solve it. ## model descriptions we present three models using both rule-based and deep learning approaches to recognize and split conditional sentences into conditional clause and other resultant categories. the rule-based model implements a pipeline that solves the end-to-end task by solving each subtask. in contrast, we considered this large task as a sequential labeling task for deep learning approach. rule-based model syntactic and semantic manually written rules are the basis for many systems that analyze technical documents, for example @xcite . in a nutshell, these approaches adopt a general architecture, which extracts the constituents of the sentence, their type and their dependency relationships using constituency or dependency parsers. among the extracted entities, there could be conditional constituents which are further analyzed. however, we have found out that for our corpus of technical documents the systems trained on standard corpora, @xcite do not perform well. a source of error might be the text itself, as the technical corpora is not necessarily written by native english speakers, but most importantly, conditional clauses have many peculiarities, both at syntactic and semantic level, which could be exploited more efficiently than a general approach does. to achieve a high quality of parsing of the conditional sentences, we developed a system of rules that proceeds sequentially to determine (i) the conditional triggers -using lexical and semantic rules (ii) boundaries of conditional clause -using syntactic and semantic rules, (iii) the border of adjacent rule-based constituents -using syntactic rules and (iv) the relationship between constituents and the corresponding conditional -using semantic rules. the conditional triggers are lexical items, like if, when, but also the comma itself, " , ", or that, when it introduces a relative clause. the rules in (i) analyze the context and decide whether a trigger does indeed introduce a conditional. then, the one predicate condition is used by the rules in (ii), that is, we determine exactly one verbal phrase whose boundaries are actually the boundaries of the conditional clause itself. we used semantic features, like imperative, or nominalization to decide whether a verbal group carries conditional relevance. some of these features are extracted directly from esg output @xcite , and some other are extracted by our rules via a deep analysis of copula, semantic transparent verbs and other verbal constructions that do not necessarily contain exactly one verb. the boundaries ambiguities are resolved by rules in (iii) , which decide whether a noun phrase is a part of a conditional or it belongs to the following clause. the rules in (iv) check for imperative, or imperative like constructions, because a clause expressing an action that logically follows a conditional must be understood as "to do" obligation. that is, these rules rank the candidates according to their imperative valency and their position with respect to the determined conditional clause. ## experiments to evaluate the performance of the models, we used the test set from the techcond dataset (section 3.3) following the data split shown in ## conclusions and future work recognizing and splitting conditional sentences for automation of business process management is a complex and important task for many industries. we presented a non-trivial real-world system that can recognize and split technical instructions (at sentence level) into condition, action, consequence clauses for business process modeling and automatic update. we defined an end-to-end task consisting of three subtasks that fits our business context and needs. we also created a new dataset and three models, using both rule-based and deep learning approaches, to solve this task. for future work, we plan to improve the task performance by 1) expanding the dataset and improving annotation to capture more examples of complex structures, and 2) trying better learning approaches or more linguistics attributes to help our models learn better.