Finding experts in any field can be difficult , especially when relying on academic publi- 11 cations given the use of jargons despite publication lists being publicly available . Within the use of 12 publications, discernment of expertise by authorship positions is often absent in the many publica- 13 tion-based expert search platforms available which could function as another discernment filter of 14 expertise. Given that it is common in many academic fields for the research group lead or lab heads 15 to take the position of the la st author , the existing authorship scoring systems that assign a decreas- 16 ing weightage from the first author would not reflect the last author correctly . To address th ese 17 mentioned problems, we incorporat ed natural language processing (Common Crawl using fastText) 18 to identify related keywords for a search compatible to using jargons as well as an authors hip posi- 19 tional scoring with the option to provide greater weightage to the last author. The resulting output 20 is a ranked scoring system of researchers upon every search which we implemented as a webserver 21 for internal agency use called ‘ APD lab Capability & Expertise Search (ACES) ’ webserver which can 22 be accessed from webserver .apdsk eg.com/aces . 23 Keywords: Auth orship ; Natural Language Processing ; Score; Weightage 24 25 1. Introduction 26 The surge of research article publications in recent years and th e use of jargon often 27 make it challenging for those not within the field to navigate through them to find and 28 discern experts. Yet, finding bona fide experts continues to grow in importance in a world 29 of ever -increasing misinformation and fake news that can lead to serious consequences 30 [1] as observed during the COVID19 pandemic [2]. The need to identify the right experts 31 in the many public /private sectors for consultan cy and collaborations can make the differ- 32 ence between success and failure . However, the definition of ‘expert’ is contentious, var- 33 ying from experience (time in field ) to the very nuanced measurement of achievements 34 that can include social impact, journal impact factors, citations, patents , commercialized 35 products among others that have different value s to the various needs. For academic uses, 36 the work here will focus on research publication s which are publicly available for easier 37 filter ing for expertise. Although publicly available, navigating through them is not easy, 38 especially when considering the authorship issues discussed in previous articles [3-6]. 39 Currently , there is a plethora of online platforms available that help identify experts 40 based on research publications such as ResearchGate, Google Scholar , Web of Science, 41 ORCID, Publons, among others . To an extent, this includes LinkedIn , which although not 42 publication based, target s a broad user base beyond academi a. While expert identification 43 based on publications is convenient , existing caveats exist in that the contribution of co - 44 Copyright: © 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/license s/by/4.0/). 2 of 7 authors are typically assumed to be equal in these scoring/ranking platforms [7]. A con- 45 tentious topic that is still debated in academia [7,8] , authorships are originally intended 46 to reflect the level of contribution by the authors to the research [8-12] and would therefore 47 be a good discernment filter in determining expertise . In fact , authorship is one of the key 48 issues discussed in collaborations as well as dedicated resources providing advice to 49 younger scientists on this area [13]. 50 Many scoring and ranking methods as those in the existing platforms lack feature s 51 to differentiate highly collaborative academic s with only middle -authorships from high 52 performing academic s with similar number of publications but as mostly first or last -au- 53 thors . Although discerning eyes could quickly differentiate that the latter is more likely to 54 have higher expertise than the former , this discernment is not reflected in the current met- 55 rics nor conveniently accessible in the current platforms . 56 To inclu de authorship as an added discernment filter , we incorporated a new scoring 57 method that credits the last author as much as the first, and implemented it as a web server 58 for assessment . Incorporating a common Crawl using fastText methods to grou p search 59 input keywords , the problem of jargons usage is mitigated through relate word search . 60 The resulting suite complements our other digitalisation efforts [14] to build an expert 61 identification system to be used by journals and grant offices to identify suitable peer re- 62 viewers. As a demonstration, this authorship and natural language processing keyword 63 search for expertise is currently based on the 2018 Google Scholar data of researchers from 64 the Agency for Science, Technology, and Research, Singapore and incorporated as the 65 APD lab Capability and Expertise Search (ACES) webserver. 66 2. Materials & Method 67 2.1. Scoring Method 68 The d efault scoring method for publication N co-authors uses the harmonic author- 69 ship credit model previously described by Hagen [15] that gives the highest weight age to 70 the first author followed by the subsequent authors. The position of the author of interest 71 (p) is determined from the left of the author list and is an element of positive real numbers 72 (1, 2, 3, 4…) . 73 score = 1 p ΣN i=11 i (1) With selection of the option to credit the last author and second last with equal 74 weightage as that of the first and second respectively , the author score would be calcu- 75 lated using equations ( 2) to ( 5) represented by the position of the author of interest (p) in 76 the (i) first or last position : 77 score = 1+1 2 2(ΣN i=11 i) (2) (ii) second or second last position: 78 score = 1+1 2+1 3 6(ΣN i=11 i) (3) (iii) any position between the second and second last position of the author list: 79 3 of 7 score = 1 p+2 ΣN i=11 i (4) and an exception for (iv) N = 3 where the author of interest is in the middle position. 80 score =1 3 ΣN i=11 i (5) 2.2. WebServer Design 81 ACES is a web application developed using Hypertext Markup Language (HTML), 82 Cascading Style Sheets (CSS) and JavaScript for the front -end, and Python version 3.7.5 83 using Flask version 1.1.1 framework for the back -end server. 84 When the user first keys in a subject keyword on the main page, ACES retrieves rel- 85 evant publications in the database by relevance and assigns a score to the publication 86 based on distance to the keyword , followed by tabulating the scores of the researcher 87 based on authorship position . The results are then displayed showing the ranked scores 88 of the relevant researchers . The total number of publications, citations and most related 89 publications pertaining to the search query are then displayed under each author . 90 2.3. Database 91 The sampling of 700 public Google Scholar profile s of researcher s affiliat ed with the 92 Agency for Science, Technology, and Research (A*STAR), Singapore were collected in 93 2019 and stored in a NoSQL database. The back -end server receives the data in JavaScript 94 Object Notation (JSON) format containing the researchers ’ and their publication infor- 95 mation of: name s, affiliation s, total number of citations and publications, research article 96 titles with the authors list, and 30 most impor tant keywords associated with the re- 97 searcher. Applying the term fre quency–inverse document frequency (tf -idf), a numerical 98 statistic to determine the importance of a word in a given document [16] onto the publi- 99 cation titles as a whole , relevance to keywords was computed . 100 2.4. Search 101 The search algorithm consists of three main phases: query processing, researcher re- 102 trieval and ranking. It utilizes the Natural Language Toolkit (NLTK) version 3.4.4 and 103 Gensim version 3.8.0 libraries. 104 2.4.1. Query Processing 105 106 Figure 1 . Main page of the w ebsite with the option to check the ‘last author corresponding’ option. 107 Upon activating the search (see Figure 1), the search query processing phase starts 108 by filtering out punctuation marks and stop words i.e. insignificant words that appear 109 frequently in English that typically frame sentences [16]. Precompile d stop words were 110 supplied by NLTK and further modified to enable stringent keyword filtering and in- 111 crease the accuracy of the search query. 112 2.4.2. Retrieval of Researchers 113 4 of 7 ACES scores the relevance of the processed search query in keywords of every re- 114 searcher. Using Gensim NLP library ( https://radimrehurek.com/gensim/ ), two -million - 115 word vectors with 300 dimensions were loaded on the back -end server. These word vec- 116 tors were trained on Common Crawl ( https://commoncrawl.org ) using fastText [17], and 117 are used to map the pr ocessed query to its corresponding values. 118 By computing the cosine similarity between word vectors of the search query and the 119 30 most important keywords of each researcher, a relevancy score is calculated. For faster 120 result out put, a batch of the top 20 high scoring researchers based on keywords are first 121 shown. 122 123 2.4.3. Ranking 124 The ranking phase co mprises of three steps: word processing, article scoring and the 125 ordering of researchers. The publication titles are processed in the same way as the search 126 query, where the punctuation marks and stop words are filtered ou t before the cosine 127 similarity between the word vectors are computed . This is followed by the authorship 128 computation which utilizes the harmonic allocation method where the highest score is 129 assigned to the first author and decreases for each subsequent author in the list [15]. 130 Excluding publications with low relevance to the query keyword, overall scores are 131 assigned to the researcher based on the sum of relevance and authorship position scores. 132 The top 20 researchers are ranked based on the combined score s and displayed on the 133 webpage, togethe r with their top five relevant publications and other information availa- 134 ble in the database . Publications of l ow relevance are greyed out, allowing the user to 135 focus only computationally deemed relevant ones (see Figure 2). The researcher names 136 and publications are shown as hyperlinks to a Google search for easily follow -up and 137 twenty researchers are displayed per page. 138 139 Figure 2 .Example of a researcher in the result s page. 140 3. Results and Discussion 141 In this work, we buil t the ACES web server as a resource for discerning and finding 142 academic experts that considers publication authorship positions . While there are exist- 143 ing algorithms such as the harmonic authorship credit model that score researchers 144 based on the authorship positions, the last author is mis -credited. This is especially so 145 where in many academic disciplines, the research group lead and in some cases, the per- 146 son who conceptualized and directed the research, especially in PhD student publica- 147 5 of 7 tions , is the last author. In cases of collaborations between groups where the other re- 148 search group senior author occupies the second last position, we assigned weightage 149 similar to that of the second author. Beyond authorship to facilitate the search of jargons 150 in publication titles , a natural language processing toolkit for word matching of the 151 search keyword was added alongside other utility features. 152 3.1. Exact word matching and Autocorrect 153 The search algorithm in ACES excludes phrases or words with ambiguous meanings 154 to prevent confounding the scoring. For exact word search, users can utilize double quo- 155 tation marks to filter out publication titles . 156 Since typographical /spelling errors are inevitable we incorporated a ‘suggested 157 word’ feature using the Python package autocorrect version 0.4.4. Although a search with 158 the error would still be performed, an autocorrect suggestion below the search bar will be 159 displayed with the the results (see Figure 3) to which a new search with the suggested 160 correct word can be re -performed by clicking on the hyperlink. 161 162 Figure 3 . Autocorrect suggestion for the misspelled word of ‘cancer’ 163 3.2. Deep Search 164 ACES was designed to provide nearly instantaneous results with a slight compro- 165 mise of accuracy. For higher accura cy, a deep search f eature was added (see Figure 1 & 3) 166 to skip the phase of fetching just the top 20 researchers based on their keywords , but to 167 perform matching throughout the whole database. While this will take a slightly longer 168 time to compute the results, all the researchers in the database w ould be computed . We 169 have incorporated this default fast search given that the current small database of about 170 700 researchers is likely to grow by many folds when implemented towards a global use 171 than from our current i nternal usage . 172 3.3. Authorship contribution 173 The reflection of contribution by authorship positions varies in different research 174 fields [11,12] , making the harmonic allocation [15] method insufficient for many academic 175 fields where the last author position is the principal inves tigator or research lead who can 176 play as important if not more than the first author [12]. To allow for better credit to last 177 authors, a checkbox below the search bar labelled ‘Last author corresponding’ (see Figure 178 1) was included as an option. This allows the field -specific weightage given to the last 179 author a high score equal to the first author (Table A1 ) and the second -last as the second . 180 This is made possible via a modified version of the default harmonic allocation. Hence, 181 this option will yield different ranking results for researchers with more last author pub- 182 lications . While it remains contentious whether the last author s should have more or equal 183 credit to the first author s, we have attributed equal corresponding weightage for ease of 184 implementation. Nonetheless, this option should be used only for the relevant fields to 185 prevent confounding the outcomes. 186 6 of 7 187 Figure 4. Search result display of “ allergy” before (left) and after (right) last author option is se- 188 lected showing different ranking when considering last authorship. 189 Since the general user is likely to focus only on the top few ranked individuals for 190 expertise, the order can matter. As a blinded demonstration (Figure 4) , when applying the 191 last author weighta ge, there would be a reordering of ‘Scientist 1 ’ which may have more 192 middle -author publications than the other preceding researchers . While the movement 193 here is perhaps small, such reshuffling could be fairly notable in a larger list , demonstrat- 194 ing the relevance of last author weightage scoring as an added discernment filter. 195 We acknowledge that the scoring method is not comprehensive and that there re- 196 mains many variations in last authorship and the possibility of slighting the first authors 197 further as well as the even more contentious second last authorship which may not reflect 198 a senior author in many cases . These problems arise from the lack of a universa l standard 199 and nuances are likely to persist alongside the definition of ‘expertise’ that is also depend- 200 ent on the very different need of different groups. Yet, in the meantime, for the purposes 201 of expert identification within academic settings, the weight age adjustment for the last 202 author may allow new upcoming research leads better visibility. 203 The current ACES website is available on webserver .apdskeg.com/aces on both desk- 204 top and smart phone browsers. A video demonstrating the use of ACES is shown in 205 https://fb.watch/57DYA4KBrj . 206 5. Conclusion 207 ACES demonstrates the incorporat ion of NLP for jargon search and a modified last - 208 author scoring method for identification of academia experts from their publications. With 209 first and last-author scoring s, due credit is also given to research leads, and provides an 210 added layer of discernment. Together, ACES allows for a search for experts based on pub- 211 licly available lists of publication. With added features of deep search, it can be easier for 212 those outside the specific fields to find experts for business consultancy and collaborators 213 in business , public health, and research . 214 Author Contributions: Software: WLW, KFC, OT, NBO and DG. Supervision: WLW, KFC and 215 SKEG. Data curation: KFC. Conceptualization: SKEG 216 Funding: This research was funded by A*STAR Office of Gran ts Administration (OGA). 217 Institutional Review Board Statement: Not applicable. 218 Informed Consent Statement: Not applicable as the data is from public domain. 219 7 of 7 220 Data Availability Statement: ACES belong s to the Agency for Science, Technology, and Research 221 of Singapore, and is for internal use. Interested parties may write to the corresponding author who 222 may make the request to the relevant departments. 223 Acknowledgments: We acknowledge Ms Ei -leen Tan of the A*Office of Grants Administration, Sci- 224 ence and Engineering Research Council (SERC) grant office for the funding of this work. SKEG 225 thank s Ebenezer for support behind the scenes. 226 Conflicts of Interest: There is no conflict of interest. 227