# Natural Language Processing Tools and Workflows for Improving Research Processes
## 1 Introduction  
Information search technology has historically lagged behind advances in storage technology [1]. Advancements in storage were driven by the need for greater availability and portability, while advancements in information retrieval were driven by the need for greater accessibility. Improving availability tends to exacerbate accessibility. Today's keyword-based search capabilities are both predicated on and limited by database management system (DBMS) capabilities. Databases store information in tabular form, are often indexed, and are retrieved using a declarative structured query language (SQL). Queries are formulated in terms of three primary clauses: the SELECT clause specifies which data elements to return; FROM specifies data sources; and the WHERE clause specifies conditions that filter data from sources. The SQL query language supports logical operators and the LIKE operator, which can find partial text matches, and together, these underpin the keyword search capability. As the Internet improved the storage of information, it was only natural to extend DBMS-like keyword-based search capabilities to the Internet, making keyword-based queries the de facto standard way of searching for information. Given the underlying technology, keyword-based search is fundamentally a sampling and filtering process that explicitly depends on a particular corpus. As illustrated in Figure 1, the typical research workflow begins with an iterative keyword refinement process that terminates when there are sufficiently relevant results. Search results also depend on the information stored in a search provider's database, but that coverage is unknown since there is no equivalent of a digital card catalog for online resources to know what has (or has not) been indexed. Also, whereas professional librarians used to add classifications and related topics to card catalogs, there does not appear to be analogous and transparent processes for classifying and relating much of today's information. Keyword selection is an iterative trial-and-error process that does not always result in the relevant literature. For example, a Scopus search using the keyword combination \"vehicle AND motion AND planning\" returned 14,540 results. When ordered by the number of citations, the 8 th result was the paper titled \"Hydrogen fueled internal combustion engine\" [2]. This paper, along with many top ranked documents, was not relevant. This work attempts to improve the research process by adapting natural language processing (NLP) techniques to more methodically and less subjectively identify pertinent keywords, topics, and papers. NLP algorithms automate the process of keyword refinement for research literature search by automatically stemming similar words, stratifying a topic into subtopics and validating keyword choices. We achieve this by incorporating three levels of abstraction over keywords, namely semantics, topics and corpus, as detailed in Section 3. The use of natural language processing (NLP) technologies has been comprehensively explored to improve keyword refinement, simplify the task of screening different repositories, and for contextualizing topics. The novelty of this research primarily lies on the integration of a number of NLP techniques into unique workflows, including word2 vec, the Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA), and Latent Semantic Indexing (LSI), as well as classifiers such as the Naive Bayes and the Adaptive Boost. This paper is organized as follows: Section 2 reviews comparable efforts to improve the research process, which helps to contextualize the current work. Section 3 describes the  
Figure 1: Data flow diagrams for a customary keyword search. The Level-1 DFD in subfigure (**a**) emphasizes the inputs and outputs. The Level-2 DFD in subfigure (**b**) shows a typical waterfall process that progresses from refinement to filtering and has two feedback loops.