Original: Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning
Revised: Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning

Original: Anonymous ACL submission

Original: Abstract
Revised: Abstract

Original: Works on learning job title representation aremainly based on Job-Transition Graph , builtfrom the working history of talents.
Revised: Works on learning job title representation are mainly based on Job-Transition Graph , built from the working history of talents.

Original: However, since these records are usually messy, thisgraph is very sparse, which affects the quality of the learned representation and hindersfurther analysis.
Revised: However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis.

Original: To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job titlerepresentation.
Revised: To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation.

Original: Specifically, we construct JobTransition-Tag Graph , a heterogeneous graphcontaining two types of nodes, i.e., job titlesand tags (i e., words related to job responsibilities or functions).
Revised: Specifically, we construct JobTransition-Tag Graph , a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functionalities).

Original: Along this line, we reformulate job title representation learning as thetask of learning node embedding on the JobTransition-Tag Graph .
Revised: Along this line, we reformulate job title representation learning as the task of learning node embedding on the Job-Transition-Tag Graph .

Original: Experiments on twodatasets show the interest of our approach
Revised: Experiments on two datasets show the interest of our approach.

Original:  Introduction
Revised:  Introduction

Original: The learning of job title  representation has receivedmuch attention in the recruitment field because thelearned representation is beneficial to various tasks,such as job recommendation (Dave et al., 2018;
Revised: The learning of job title (referred to as job for short) 1 representation has received much attention in the recruitment field because the learned representation is beneficial to various tasks, such as job recommendation (Dave et al., 2018;

Original: Liu et al., 2019b), job title benchmarking (Zhanget al., 2019), and job mobility prediction (Zhanget al., 2021).
Revised: Liu et al., 2019b), job title benchmarking (Zhang et al., 2019), and job mobility prediction (Zhang et al., 2021; Zhu et al., 2021).

Original: However, in practice, learning a goodrepresentation is challenging for the following reasons: (i) Noisy data : job title data is noisy due topersonal subjective reasons (i.e., spelling errors)or objective reasons (i.e., resume parsers are notperfect).
Revised: However, in practice, learning a good representation is challenging for the following reasons: (i) Noisy data : job title data is noisy due to personal subjective reasons (i.e., spelling errors) or objective reasons (i.e., resume parsers are not perfect).

Original: (ii) Messy data : job titles are messy because people have different ways of thinking, andnaming conventions vary by company and industry.
Revised: (ii) Messy data : job titles are messy because people have different ways of thinking, and naming conventions vary by company and industry.

Original: For example, there are many alternative job titlesfor the same position, e.g., “purchasing clerk” and“buyer”.
Revised: For example, there are many alternative job titles for the same position, e.g., “purchasing clerk” and “buyer”.

Original: Another problem is that due to the ambiguity of certain terms, they can refer to different positions in different contexts, e.g., “registered nursessandwich rehab” and “sandwich maker”.
Revised: Another problem is that due to the ambiguity of certain terms, they can refer to different positions in different contexts, e.g., “registered nurses sandwich rehab” and “sandwich maker”.

Original: For thesereasons, standard semantic-based approaches thataggregate (e.g., mean or sum) word representations to get job title semantic representation may lead to mismatches.
Revised: For these reasons, standard semantic-based approaches that aggregate (e.g., mean or sum) word representations to get job title semantic representation may lead to mismatches.

Original: Moreover, these methods ignore hidden relationships between job titles, e.g., titles in the same resume may be similar.
Revised: Moreover, these methods ignore hidden relationships between job titles, e.g., titles in the same resume may be similar.

Original: (Dave et al., 2018; Zhang et al., 2019) learn representations from graphs.
Revised: (Dave et al., 2018; Zhang et al., 2019) learn representations from graphs.

Original: They create graphs from career trajectories, where nodes represent job titles, edges represent job transitions.
Revised: They create graphs from career trajectories, where nodes represent job titles and edges represent job transitions.

Original: Then they design different loss functions to embed the nodes into a lowdimensional space.
Revised: Then they design different loss functions to embed the nodes into a low-dimensional space.

Original: However, the generated graphs are usually sparse due to the above reasons, limiting the performance of graph-based methods.
Revised: However, the generated graphs are usually sparse due to the above reasons, limiting the performance of graph-based methods.

Original: Standardizing job titles before generating graphs can alleviate the sparsity issue to a certain extent, but at the cost of losing some information.
Revised: Standardizing job titles before generating graphs can alleviate the sparsity issue to a certain extent, but at the cost of losing some information.

Original: To tackle these challenges, we propose to enrich graphs with structured contextual information and learn job title representations through network embedding methods.
Revised: To tackle these challenges, we propose to enrich graphs with structured contextual information and learn job title representations through network embedding methods.

Original: Specifically, inspired by domain-specific Named 
Revised: More specifically, inspired by domain-specific Named Entity tags (i.e.,

Original: Entity tags (i.e., RES ponsibility and FUN ction) proposed in (Liu et al., 2019a), we treat the job title as a combination of responsibilities, functionalities, and other additional information.
Revised: RES ponsibility and FUN ction) proposed in (Liu et al., 2019a), we treat the job title as a combination of responsibilities, functionalities, and other additional information.

Original: Words related to responsibility and functionality are defined as tags.
Revised: Words related to responsibility and functionality are defined as tags.

Original: We assume that job titles with the same tag describe similar job functions or responsibilities, so they are more likely to have similar representations.
Revised: We assume that job titles with the same tag describe similar job responsibilities or functionalities, making them more likely to have similar representations.

Original: Along this line, we construct Job-Transition-Tag Graph , a heterogeneous graph containing two types of nodes, i.e., job titles and tags, which carries more information, thereby alleviating the sparsity problem.
Revised: Along this line, we construct Job-Transition-Tag Graph , a heterogeneous graph containing two types of nodes, i.e., job titles and tags, which carries more information, thereby alleviating the sparsity problem.

Original:  Methodology 
Revised:  Methodology 2. Preliminaries

Original: 2. Preliminaries

Original: A graph/network is represented as G =
Revised: A graph/network is represented as G =

Original: ( V , E ) , with node set V and edge set E .
Revised: ( V , E ) , with node set V and edge set E .

Original: Nodes and edges can optionally have a type, so a graph can be homogeneous or heterogeneous.
Revised: Nodes and edges can optionally have a type, so a graph can be homogeneous or heterogeneous.

Original: In the recruitment field, the career trajectory of talents can be repre-  sented by graphs.
Revised: In the recruitment field, the career trajectory of talents can be represented by graphs.

Original: Formally, consider a job seekerset U and their working history set H
Revised: Formally, consider a job seeker set U and their working history set H

Original: = { H u } u ∈U ,where the working history of each u is representedas a sequence of n work records ordered by time
Revised: = { H u } u ∈U , where the working history of each u is represented as a sequence of n work records ordered by time

Original: H u
Revised: H u

Original: = { J 1 , . . .
Revised: = { J 1 , . . .

Original: J n } .
Revised: J n } .

Original: The i -th record J i is denotedby ( j i , p i ) , indicating that u is engaged in a position (titled j i ) during the p i period.
Revised: The i -th record J i is denoted by ( j i , p i ) , indicating that u is engaged in a position (titled j i ) during the p i period.

Original: The set of jobtitles j i that occurred in H is denoted as J .
Revised: The set of job titles j i that occurred in H is denoted as J .

Original: Basedon H , Job-Transition Graph (Figure 1a) can beconstructed, which is formally defined as:
Revised: Based on H , Job-Transition Graph (Figure 1a) can be constructed, which is formally defined as:

Original: Definition 1 (Job-Transition Graph) is definedas a directed homogeneous graph G jj
Revised: Definition 1 (Job-Transition Graph) is defined as a directed homogeneous graph G jj

Original: =
Revised: =

Original: ( J , E jj )generated from H , where J is a set of job titles,and the edge e jjxy ∈
Revised: ( J , E jj ) generated from H , where J is a set of job titles, and the edge e jjxy ∈

Original: E jj represents the job transition from the former job j x to the next job j y .
Revised: E jj represents the job transition from the former job j x to the next job j y .

Original: 2.1. Learning from Job-Transition Graph: 
Revised: 2.1. Learning from Job-Transition Graph: An Overview

Original: An Overview

Original: G jj is often used for job title representation learning tasks.
Revised: G jj is often used for job title representation learning tasks.

Original: The current procedure isto first  build a
Revised: The current procedure is first to build a

Original: G jj , and then learn job title representation from it.
Revised: G jj , and then learn job title representation from it.

Original: More specifically, (Dave et al., 2018) first builds
Revised: More specifically, (Dave et al., 2018) first builds

Original: G jj and  other two graphs.
Revised: G jj and the other two graphs.

Original: Then, the Bayesianpersonalized ranking and margin-based loss functions are used to learn job title representations fromgraphs.
Revised: Then, the Bayesian personalized ranking and margin-based loss functions are used to learn job title representations from graphs.

Original: Job2Vec (Zhang et al., 2019) constructsa G jj , where the node denotes  job title affiliatedwith the specific company, and a multi-view representation learning method is proposed.
Revised: Job2Vec (Zhang et al., 2019) constructs a G jj , where the node denotes a job title affiliated with the specific company, and a multi-view representation learning method is proposed.

Original: (Zhanget al., 2021) adds company nodes in G jj to build aheterogeneous graph.
Revised: (Zhang et al., 2021) adds company nodes in G jj to build a heterogeneous graph.

Original: Then they use a graph neural network to represent the nodes.
Revised: Then they use a graph neural network to represent the nodes.

Original: As mentionedabove, the job title and job transition data are messy.
Revised: As mentioned in Section 1, the job title and job transition data are messy.

Original: Therefore, G jj may be sparse (Zhang et al., 2019).
Revised: Therefore, G jj may be sparse (Zhang et al., 2019), which we will further prove in Section 3.1.

Original: To alleviate this issue, a simple method is to standardize job  titles.
Revised: In order to alleviate this issue, a simple method is to standardize job titles and then construct a normalized and denser graph based on the standardized job titles.

Original: For example, (Dave et al., 2018)normalizes titles by using Carotene (Javed et al.,2015), (Zhang et al., 2019) aggregates titles by filtering out low frequency words, and (Zhang et al.,2021) unifies titles according to IPOD (Liu et al.,2019a).
Revised: For example, (Dave et al., 2018) normalizes titles by using Carotene (Javed et al., 2015), (Zhang et al., 2019) aggregates titles by filtering out low frequency words, and (Zhang et al., 2021) unifies titles according to IPOD (Liu et al., 2019a).

Original: However, the standardization of job titlesmay lose some specific information.
Revised: However, the standardization of job titles may lose some specific information.

Original: Furthermore,these methods either ignore the semantic information contained in job titles (Dave et al., 2018; Zhanget al., 2021) or separate the semantic informationfrom the graph topology (Zhang et al., 2019).
Revised: Furthermore, these methods either ignore the semantic information contained in job titles (Dave et al., 2018; Zhang et al., 2021) or separate the semantic information from the graph topology (Zhang et al., 2019).

Original: 2.1. Job Title Composition
Revised: .1. Job Title Composition

Original: Generally, a job title consists of three parts (Liuet al., 2019a; Zhang et al., 2019): (i) Responsibility : describes the role and responsibility of a position from different levels (e.g., director, assistant,and engineer).
Revised: Generally, a job title consists of three parts (Liu et al., 2019a; Zhang et al., 2019): (i) Responsibility : describes the role and responsibility of a position from different levels (e.g., director, assistant, and engineer).

Original: (ii) Functionality : describes the business function of a position from various dimensions (e.g., sales, national and security).
Revised: (ii) Functionality : describes the business function of a position from various dimensions (e.g., sales, national and security).

Original: (iii) Additional Information : contains personal-specific information.
Revised: (iii) Additional Information : contains personal-specific information.

Original: We denote the words related to responsibility and functionality as tags, and they form a tag set T .
Revised: We denote the words related to responsibility and functionality as tags, and they form a tag set T .

Original: These tags are the essence of the job title and provide important information about the position.
Revised: These tags are the essence of the job title and provide important information about the position.

Original: However, few works directly include this information in the representation learning scheme.
Revised: However, few works directly include this information in the representation learning scheme.

Original: In this paper, we consider these tags when generating graphs.
Revised: In this paper, we consider these tags when generating graphs.

Original: These tags can alleviate the graph sparsity problem of G jj and provide additional information for the learning of job title representations.
Revised: These tags can alleviate the graph sparsity problem of G jj and provide additional information for the learning of job title representations.

Original: 2. Proposed Graphs
Revised: 2. Proposed Graphs

Original: In order to address the sparsity issue of G jj , we consider adding more information when generating graphs, i.e., tags related to job responsibilities or functions, driven by the job title composition.
Revised: In order to address the sparsity issue of G jj , we consider adding more information when generating graphs, i.e., tags related to job responsibilities or functionalities, driven by the job title composition.

Original: Along this line, we define various types of graphs:
Revised: Along this line, we define various types of graphs:

Original: Definition 2 (Enhanced Job-Transition Graph) is based on G jj with additional enhanced edges.
Revised: Definition 2 (Enhanced Job-Transition Graph) is based on G jj with additional enhanced edges.

Original: It is defined as G jjE =
Revised: It is defined as G jjE =

Original: ( J , E jj ∪ E jjE ) , where E jjE is a set of enhanced edges.
Revised: ( J , E jj ∪ E jjE ) , where E jjE is a set of enhanced edges.

Original: More specifically, if j x and j y share a word w , then we add a bi-directional edge between them, i.e., e jjxy and e jjyx .
Revised: More specifically, if j x and j y share a tag w , then we add a bi-directional edge between them, i.e., e jjxy and e jjyx .

Original: As shown in Figure 1b,  “purchasing manager” shares the tag “purchasing” with “purchasing clerk”, so we add an enhanced edge (i.e., the red dashed line) between them.
Revised: As shown in Figure 1b, red dashed lines represent additional enhanced edges, e.g., “purchasing manager” shares the tag “purchasing” with “purchasing clerk”, so we add edges between them.

Original: Definition 3 (Job-Tag Graph) is  a heterogeneous graph G jt =
Revised: Definition 3 (Job-Tag Graph) is defined as a heterogeneous graph G jt =

Original: ( J ∪ T , E jt ) , with job titles and tags, two node types.
Revised: ( J ∪ T , E jt ) , with job titles and tags, two node types.

Original: E jt is a set of bi-directional edges between a job title and a tag, representing the “has/in” relationship.
Revised: E jt is a set of bi-directional edges between a job title and a tag, representing the “has/in” relationship.

Original: An example of the “has/in” relationship is given in Figure 1c (i.e., the green line), where the job title “automotive technician” has a tag “automotive”, and “automotive” is in “automotive technician”.
Revised: An example of the “has/in” relationship is given in Figure 1c (i.e., the green line), where the job title “automotive technician” has a tag “automotive”, and “automotive” is in “automotive technician”.

Original: In order to aggregate more information, we further combine Job-Transition Graph and Job-Tag Graph to build Job-Transition-Tag Graph :
Revised: In order to aggregate more information, we further combine Job-Transition Graph and Job-Tag Graph to build Job-Transition-Tag Graph :

Original: Definition 4 (Job-Transition-Tag Graph) is  a heterogeneous graph G jtj =
Revised: Definition 4 (Job-Transition-Tag Graph) is defined as a heterogeneous graph G jtj =

Original: ( J ∪ T , E jj ∪ E jt ) with two node types and two edge types.
Revised: ( J ∪ purchasing manager staff account purchasing manager automotive [Figure] manager automotive technician purchase agent staff accountant purchasing [Figure]

Original: Inspired by the achievements of network embedding models in the node representation learning problem (Hamilton et al., 2017), we apply differ- purchasing manager staff account purchasing manager automotiveshop manager automotive technician purchase agent staff accountant purchasing clerk telemarketer customer service (a) Job-Transition graph.
Revised: telemarketer customer service (a [Figure] graph. purchasing manager staff account purchasing manager automotive

Revised: [Figure]

Original: purchasing manager staff account purchasing manager automotive shop manager automotive technician purchase agent staff accountant purchasing clerk telemarketer customer service purchasing (b) Enhanced Job-Transition graph.
Revised: manager automotive technician purchase agent staff accountant purchasing clerk telemarketer customer service purchasing ( [Figure] Enhanced Job [Figure]

Revised: Transition graph.

Original: purchasing manager staff account purchasing manager automotive shop manager automotive technician purchase agent staff accountant purchasing clerk telemarketer customer service technician automotive manager purchasing purchase staff accountant (c) Job-Tag graph. 
Revised: purchasing manager staff account [Figure] manager automotive shop manager automotive technician purchase agent staff accountant purchasing clerk telemarketer customer service technician automotive manager purchasing purchase staff accountant (c) Job-Tag graph. purchasing manager

Original: purchasing manager staff account purchasing manager automotive shop manager automotive technician purchase agent staff accountant purchasing clerk telemarketer customerservice technician automotive manager purchasing purchase staff accountant
Revised: [Figure] account purchasing manager automotive shop manager automotive technician purchase agent [Figure] accountant purchasing clerk telemarketer customer

Original: [Figure]
Revised: [Figure]

Revised: technician automotive manager purchasing

Revised: [Figure]

Original: ent network embedding models to learn job titlerepresentation from the graphs defined above.
Revised: staff accountant [Figure] T , E jj ∪ E jt ) with job titles and tags, two node types, and transition and “has/in”, two edge types. Inspired by the achievements of network embedding models in the node representation learning problem (Hamilton et al., 2017), we apply different network embedding models to learn job title representation from the graphs defined above.

Original:  Experiments
Revised:  Experiments

Revised: We evaluate the proposed job title representation learning scheme through (i) a node classification task (i.e., job title classification ) and (ii) a link prediction task (i.e., next-job prediction ).

Revised: This section will first describe the two datasets used and experimental settings, followed by discussing the results.

Original: 3. Datasets
Revised: 3. Datasets

Original: CareerBuilder12 (CB12): an open dataset froma Kaggle competition.
Revised: CareerBuilder12 (CB12): an open dataset from a Kaggle competition.

Original: It contains a collection ofworking experiences represented by sequences ofjob titles.
Revised: It contains a collection of working experiences represented by sequences of job titles.

Original: For the node classification task, we use
Revised: For the node classification task, we use

Original: AutoCoder 2 to assign a SOC 2018 to each job title.
Revised: AutoCoder 3 to assign a SOC 2018 to each job title.

Original: The labeling details are given in Appendix A.1.
Revised: The labeling details are given in Appendix A.1.

Original: Randstad: a private French resume dataset provided by Randstad company, where each resume isparsed into multiple sections, an example is givenin Figure 3 of Appendix.
Revised: Randstad: a private French resume dataset provided by Randstad company, where each resume is parsed into multiple sections, e.g., PersonalInformation , EducationHistory and EmploymentHis- tory . An example is given in Figure 3 of Appendix.

Original: Graphs are built from EmploymentHistory section.
Revised: Graphs are built from EmploymentHistory section.

Original: # C # W
Revised: Both datasets use a tree-like taxonomy, as described in Appendix A.1 and A.2. For example, from root class to leaf class, the CB12 taxonomy is organized as MajorGroup → MinorGroup → BroadGroup → DetailedOccupation .

Original: [Equation]
Revised: Consistent with the reality of the recruitment market, some occupations rarely appear in both datasets. To balance the data, we filter out occupations with fewer than 200 occurrences, i.e., MinorGroup for CB and JobGroup for Randstad . Also, for graph construction, we remove working histories with less than two work records, i.e., | H u | < 2 .

Original:  |E[Equation]
Revised: # C # W |J | |E

Original: E [Equation]
Revised: jj | |E jjE | |E

Original:  |
Revised: jt |

Original: [Table]
Revised: [Table]

Original: - sents the vocabulary size for node one-hot encoding.
Revised: In order to generate tags, we first tokenize titles into tokens and remove stopwords, numbers, and punctuation.

Original: For both datasets, we use the Top-200 tokens thatappear most frequently in job titles and belong to
Revised: Then, we use the Top-200 tokens that appear most frequently in job titles and belong to

Original: IPOD (Liu et al., 2019a) as tags.
Revised: IPOD (Liu et al., 2019a) as tags.

Original: The details of taggeneration are given in Appendix A.3.
Revised: The details of tag generation are given in Appendix A.3.

Original: We assignthe one-hot encoding of the corresponding title for each title node as the node feature.
Revised: We assign the one-hot encoding of the corresponding title for each title node as the node feature.

Original: The vocabulary set is obtained by filtering words with a frequency of 1 from the tokenized job titles. 
Revised: The vocabulary set is obtained by filtering words with a frequency of 1 from the tokenized job titles. Statistics for [Table] W2V

Original: Statistics for datasets and graphs are summarized in Table 1.
Revised: [Table] Job-Tag-Job datasets and graphs are summarized in Table 1.

Original: We can observe that G jj (i.e., |V| and |E jj | ) are sparse.
Revised: We can observe that G jj (i.e., |J | and |E jj | ) are sparse.

Original: 3. Experimental Settings
Revised: 3. Experimental Settings

Original: We evaluate the proposed job titlerepresentation learning scheme through (i) a node classification task (i.e., job title classification ) and (ii) a link prediction task (i.e., next-job prediction ).
Revised: For job title classification task: we classify job titles into root categories in this work, i.e., MajorGroup for CB12 and JobClass for Randstad .

Original: For job title classification , we randomly split the data into training/validation/test sets with a ratio of 60%/20%/20%.
Revised: We randomly split the data into training/validation/test sets with a ratio of 60%/20%/20%.

Revised: For next-job prediction task: we treat it as a link prediction task on Job-Transition Graph to predict whether there exists an edge (transition) between two nodes (job titles).

Original: We keep the same split ratio on positive/negative edges for next-job prediction , where negative edges are randomly picked from unconnected node pairs (i.e., the same size as positive edges). 
Revised: We keep the same split ratio on positive/negative edges, where negative edges are randomly picked from unconnected node pairs (i.e., the same size as positive edges). We evaluate the performance of our proposed learning scheme against the following baselines.

Original: The baselines used are listed below:
Revised: A detailed description of these baselines is provided in Appendix A.4.

Original: • Homogeneous : Node2Vec (N2V) (Grover and Leskovec, 2016), GCN (Kipf and Welling, 2016) and GAT (Veliˇckovi´c et al., 2017).
Revised: • Homogeneous : Node2Vec (N2V) (Grover and Leskovec, 2016), GCN (Kipf and Welling, 2017) and GAT (Velickovic et al., 2018).

Original: • Heterogeneous : Metapath2Vec (M2V) (Dong et al., 2017), RGCN (Schlichtkrull et al., 2018) and HAN (Wang et al., 2019b).
Revised: Heterogeneous : Metapath2Vec (M2V) (Dong et al., 2017), RGCN (Schlichtkrull et al., 2018) and HAN (Wang et al., 2019b).

Original: • Semantic-based : Word2Vec(W2V)
Revised: • Semantic-based : Word2Vec

Original:  (Le and Mikolov, 2014) and BERT (Devlin et al., 2018).
Revised: (W2V) (Le and Mikolov, 2014) and BERT (Devlin et al., 2019).

Revised: Our implementation is based on the DGL package (Wang et al., 2019a).

Revised: In both tasks, for unsupervised methods, node representations are learned from the entire dataset.

Original: For detailed parameter settings and baseline descriptions, see Appendix A.5 and A.4.
Revised: Then train the logistic regression classifier on both the training and validation sets. Each semi-supervised model is trained on the training set, and the parameters are optimized on the validation set. The final performance is evaluated on the test set. To ensure fairness,

Revised: we

Revised: [Table]

Revised: keep the same data split for both methods, repeat each prediction experiment ten times, and report the average performance scores (i.e., Macro-F1 and

Revised: Micro-F1 for job title classification and AUC for next-job prediction ).

Revised: For details of other parameter settings, see Appendix A.5.

Original: 3. Results 
Revised: 3. Results 3.3. Job Title Classification

Original: Job Title Classification

Original: Table 2 summarizes the best results of all methods on different graphs.
Revised: Table 2 summarizes the best results of all methods on different graphs.

Original: We have the following obser- [Table] vations: (i) Among all graphs, all models generallyhave the lowest scores on G jj because this graph isoften sparse and can only provide limited information.
Revised: We have the following observations: (i) Among all graphs, all models generally have the lowest scores on G jj because this graph is often sparse and can only provide limited information.

Original: (ii) All models perform better on G jjE (except
Revised: (ii) All models perform better on G jjE (except

Original: Macro-F1 of GCN ) than G jj , which shows thatthe enhanced edges provide additional information.(iii)
Revised: Macro-F1 of GCN ) than G jj , which shows that the enhanced edges provide additional information.

Revised: One interpretation for enhanced edges is adding semantic information, i.e., two job titles are more likely to be similar if they share the same word, represented by edges from the graph perspective.

Revised: (iii)

Original: The heterogeneous models perform well on ourproposed G jtj , which indicates that the added tagnodes can effectively improve the quality of representation.
Revised: The heterogeneous models perform well on our proposed G jtj , which indicates that the added tag nodes can effectively improve the quality of representation.

Original: We did not apply homogeneous methodsto G jtj , but  results on G jjE prove that the information given by tags is useful.
Revised: Note that we did not apply homogeneous methods to G jtj , but the results on G jj E prove that the information given by tags is useful.

Original: (iv) BERT has a betterperformance than W2V , but graph neural networksgenerally outperform semantic-based methods.
Revised: (iv) The models with attention mechanisms outperform the models without attention, demonstrating that the attention mechanism is good at capturing important information from noisy graphs.

Original: 3.3. Next-Job Prediction
Revised: 3.3. Next-Job Prediction

Original: We further evaluate the learning scheme using nextjob prediction, which can be viewed as a link prediction task to predict whether a position will berecommended as the next job.
Revised: We further evaluate the learning scheme using nextjob prediction , which can be viewed as a link prediction task to predict whether a position will be recommended as the next-job.

Original:  The results on CB12are shown in Table 3.
Revised: For unsupervised methods, edge features are represented by applying binary operators (Grover and Leskovec, 2016) [Table] on node pairs, and then the best binary operator is selected based on the validation set, while the dot product is used for semi-supervised methods. The results on CB12 given in Table 3 show the promising results of our proposed graphs.

Original:  BERT  performs best, followed by HAN on G jtj .
Revised: Like job title classification , the scores of all network embedding methods, i.e., N2V , GAN , M2V and HAN better on G jjE compared to G jj , and the heterogeneous models perform best on G jtj . Such results further demonstrate the effectiveness of our proposed method for constructing graphs, whether adding additional information based on tags (i.e., G jj E ) or directly adding tags to the graph (i.e., G jt and G jtj ). BERT using Hadamard operator performs best, followed by HAN on G jtj with a slight difference of 0.007.

Original: However, when we use thedot product used in HAN to obtain edge features for 
Revised: However, when we use the dot product used in HAN to obtain edge features for BERT , the

Original: BERT , the AUC  drops  to 0.476, while W2V dropsto 0.557.
Revised: AUC of BERT drops sharply to 0.477, while W2V only drops a little to 0.763.

Original: We will discuss such results in futurework.
Revised: We will discuss such results in future work.

Original: Overall, the results of link prediction alsodemonstrate the effectiveness of our method. [Table]
Revised: Overall, the results of link prediction also demonstrate the effectiveness of our proposed graphs.

Original: 3.3. Visualization
Revised: 3.3. Visualization

Original: For a more intuitive comparison, we  visualize thelearned representations  in Figure 2, and each colorcorresponds to an occupation category 3 .
Revised: For a more intuitive comparison, we select five occupations and then visualize the job title representations learned by HAN in Figure 2, with each color corresponding to an occupation category.

Original: Overall, the representations learned by HAN on all graphs are clustered into groups.
Revised: Overall, the representations learned by HAN on all graphs are clustered into groups.

Original: However, when considering tags, representations are easier to be subdivided further in each category.
Revised: However, when considering tags, representations are easier to be subdivided further in each category.

Original: For example, in Figure 2d, the orange occupation can be further divided into three sub-clusters, which proves that adding tag nodes can help capture more detailed information and make the learned representation more informative.
Revised: For example, in Figure 2d, the orange occupation can be further divided into three sub-clusters, which proves that adding tag nodes can help capture more detailed information and make the learned representation more informative.

Original: This detailed information helpsto classify positions further because we only classify job titles into the root category  in this work.[Figure]
Revised: This detailed information helps further categorize occupations, as we only classify job titles into the root category (i.e., MajorGroup ) in this work.

Original:  Conclusion
Revised:  Conclusion

Original: This paper first proposes to enrich Job-Transition
Revised: This paper first proposes to enrich Job-Transition

Original: Graph commonly used in job title representation  learning tasks by adding  tag nodes and then learn job title representations through network embedding methods.
Revised: Graph commonly used in job title representation [Figure] learning tasks by adding tag-related information or directly adding tag nodes, and then learn job title representations through network embedding methods.

Original: This graph can alleviate the sparsity problem, thereby improving the quality of learned representations, as demonstrated by node classification and link prediction tasks on two datasets.
Revised: This enhancing method can alleviate the sparsity problem in Job-Transition Graph , thereby improving the quality of learned representations, as demonstrated in the experimental results of job title classification and job title classification .

Original: Future research lines will focus on learning from weighted graphs and improving the tag generation. A Appendix
Revised: In future work, we would like to explore why the Hadamard operator and dot product lead to such different link prediction results for BERT .

Original: In the original working experience dataset provided by CareerBuilder12, job titles are not prelabeled. Therefore, for the job title classificationtask, we use an online third-party API O*Net-SOC
Revised: Furthermore, other research lines are (i) considering edge weights when learning from graphs, (ii) classifying job titles into different occupational levels, and (iii) improving the tag generation approach.

Original: AutoCoder 4 to assign a Standard Occupation Classification code (SOC) 2018 to each job title, as wellas a match score (i.e., scores above 70 means thatthe correct code is accurately predicted at least 70%of the time). SOC 2018 is a four-level taxonomystructure, including MajorGroup (23), MinorGroup(98), BroadGroup (459) and DetailedOccupation(867). For example, O*Net-SOC AutoCoder assigns the code 11-2022 (Sales Managers) for thetitle “sales director”, which belongs to the levelof DetailedOccupation . 11-2020 (Marketing and
Revised: Acknowledgment

Original: Sales Managers) is BroadGroup level, 11-2000(Advertising, Marketing, Promotions, Public Relations, and Sales Managers) is MinorGroup level,and 11-0000 (Management Occupations) is MajorGroup level. In this work, we categorize job titlesinto MajorGroup . We have annotated a total of30,000 job titles.
Revised: This work is supported by the Randstad research chair in collaboration with MICS Lab, CentraleSupélec, Université Paris-Saclay.

Original: The developer guarantees thatthe code assigned to  the title plus description hasan accuracy rate of 85%. However, only the jobtitle is provided in our experiments, so the SOC2018 code may be incorrectly assigned. For thisreason, we filtered out job titles with scores below70. Therefore, 22,590 job titles remain.
Revised: We would like to thank the Mésocentre 5 computing center of CentraleSupélec and École Normale Supérieure ParisSaclay for providing computing resources.

Original: A. Randstad Data Description
Revised: A. Randstad Data Description

Original: Figure 3 shows an example of parsed resume in
Revised: Figure 3 shows an example of parsed resume in

Original: Randstad dataset.
Revised: Randstad dataset.

Original: We build graphs from EmploymentHistory , which contains a JobTitle , and its corresponding occupation labels (i.e., JobCode , JobGroup and JobClass ).
Revised: We build graphs from EmploymentHistory , which contains a JobTitle , and its corresponding occupation labels (i.e., JobCode , JobGroup and JobClass ).

Original: The hierarchical taxonomystructure used in the Randstad dataset has a threelevel hierarchy, where JobCode s are leaf classes,and each internal ( JobGroup )/root class ( JobClass )is the aggregation of all its descendant classes.
Revised: The hierarchical taxonomy structure used in the Randstad dataset has a threelevel hierarchy, where JobCode s are leaf classes, and each internal class (i.e., JobGroup ) or root class (i.e., JobClass ) is the aggregation of all its descendant classes.

Original: There are 25 JobClass s,295 JobGroup s and [-4,
-] JobCode s, respectively.
Revised: There are 25 JobClass s, JobGroup s and [-4,
-]4,443 JobCode s, respectively.

Original: In this work, we categorizejob titles into JobClass .
Revised: In this work, we categorize job titles into JobClass .

Original: A. Tag Generation
Revised: A. Tag Generation

Original: For both datasets, we first tokenize titles into tokens and remove stopwords, numbers, and punctuation.
Revised: For both datasets, we first tokenize titles into tokens and remove stopwords, numbers, and punctuation.

Original: The word frequency distribution of words4 http://www.onetsocautocoder.com/plus/ onetmatch
Revised: The word frequency distribution of words

Original: [Figure]
Revised: [Figure]

Original: in two datasets are shown respectively in Figure 4, which are subject to the long-tail distribution, similar to the observation in (Zhang et al., 2019).
Revised: in two datasets are shown respectively in Figure 4, which are subject to the long-tail distribution, similar to the observation in (Zhang et al., 2019).

Original: Most words appear only once, i.e., 53 .
Revised: Most words appear only once, i.e., 53 .

Original: 55% of words only appear one time in CB12 dataset, and this ratio is 56 .
Revised: 55% of words only appear one time in CB12 dataset, and this ratio is 56 .

Original: 55% in Randstad dataset.
Revised: 55% in Randstad dataset.

Original: Figure further shows the top ten and last ten frequent words in each dataset.
Revised: Figurefurther shows the top ten and last ten frequent words in each dataset.

Original: Obviously, high-frequency words like “manager” and “sales” describe the re- [Figure] sponsibility or functionality of the job title, while [Figure]
Revised: Obviously, high-frequency words like “manager” and “sales” describe the responsibility or functionality of the job title, while low-frequency words are usually noise or personspecific words.

Original: low-frequency words are usually noise or person-

Original: [Figure]

Original: specific words. Based on the domain-specific NE[Figure] tags (i.e., RES ponsibility, FUN ction) proposed in
Revised: Based on the domain-specific NE tags (i.e., RES ponsibility, FUN ction) proposed in

Original: IPOD (Liu et al., 2019a), we then select the Top- [Figure] 200 tokens that appear most frequently and appear [Figure]
Revised: IPOD (Liu et al., 2019a), we then select the Top200 tokens that appear most frequently and appear in the IPOD NE tag set as tags for each dataset.

Original: in the IPOD NE tag set as tags for each dataset.

Original: [Figure]
Revised: [Figure]

Original: A. Baseline Description
Revised: A. Baseline Description

Original: We explore the following network embedding methods on our proposed graphs to learn job title representation.
Revised: We explore the following network embedding methods on our proposed graphs to learn job title representation.

Original: According to the type of graph, the network embedding methods are naturally divided into Homogeneous and Heterogeneous .
Revised: According to the type of graph, the network embedding methods are naturally divided into Homogeneous and Heterogeneous .

Original: Then, we further categorize each category into Unsupervised and Semi-Supervised according to whether node
Revised: Then, we further categorize each category into Unsupervised and Semi-Supervised according to whether node

Original: labels are provided for learning.
Revised: labels are provided for learning.

Original: Homogeneous&Unsupervised
Revised: Homogeneous&Unsupervised

Original: • Node2Vec (N2V) (Grover and Leskovec, 2016):is an extension of DeepWalk with a biasedrandom walk process for neighborhood exploration.
Revised: • Node2Vec (N2V) (Grover and Leskovec, 2016): is an extension of DeepWalk with a biased random walk process for neighborhood exploration.

Original: Homogeneous&Semi-supervised :
Revised: Homogeneous&Semi-supervised :

Original: • GCN (Kipf and Welling, 2016): is a semisupervised GNN that generalizes the convolutional operation to homogeneous graphs.
Revised: • GCN (Kipf and Welling, 2017): is a semisupervised Graph Neural Network (GNN) that generalizes the convolutional operation to homogeneous graphs.

Original: • GAT (Veliˇckovi´c et al., 2017): uses a selfattention strategy to learn the importance between a node and its neighbors.
Revised: • GAT (Velickovic et al., 2018): uses a selfattention strategy to learn the importance between a node and its neighbors.

Original: Heterogeneous&Unsupervised :
Revised: Heterogeneous&Unsupervised :

Original: • Metapath2Vec (M2V) (Dong et al., 2017): performs meta-path-guided walks and utilizes SkipGram to embed heterogeneous graphs.
Revised: • Metapath2Vec (M2V) (Dong et al., 2017): performs meta-path-guided walks and utilizes SkipGram to embed heterogeneous graphs.

Original: Heterogeneous & Semi-supervised :
Revised: Heterogeneous & Semi-supervised :

Original: • RGCN (Schlichtkrull et al., 2018): is an extension of GCN on heterogeneous graphs, introducing relation-specific transformations basedon the type of edges.
Revised: • RGCN (Schlichtkrull et al., 2018): is an extension of GCN on heterogeneous graphs, introducing relation-specific transformations based on the type of edges.

Original: • HAN (Wang et al., 2019b): proposes a hierar- chical attention mechanism, i.e., node-level andsemantic-level for heterogeneous graphs.
Revised: • HAN (Wang et al., 2019b): proposes a hierarchical attention mechanism, i.e., node-level and semantic-level for heterogeneous graphs.

Original: In addition to the comparison between networkembedding methods
Revised: In addition to the comparison between network embedding methods

Original: , we will

Original:  also compare the rep- resentation learned through graphs with the repre- sentation obtained by semantic-based 
Revised: , we will also compare the representation learned through graphs with the representation obtained by semantic-based methods.

Original: methods

Original: Semantic-based :
Revised: Semantic-based :

Original: • Word2Vec
Revised: • Word2Vec

Original: (W2V) (Le and Mikolov, 2014): the representation of a job title is obtained by aver- aging word vectors in it.
Revised: (W2V) (Le and Mikolov, 2014): the representation of a job title is obtained by averaging word vectors in it.

Original: We use word vectorstrained on Google News 5 for CB12 , and a pretrained French embedding model (Fauconnier,2015) for Randstad .•
Revised: We use word vectors trained on Google News 7 for CB12 , and a pretrained French embedding model (Fauconnier, 2015) for Randstad .

Original:  BERT (Devlin et al., 2018) : the job title representations are obtained by using bert-as-servicepackage (Xiao, 2018).
Revised: • BERT (Devlin et al., 2019): the job title representations are obtained by using the bert-asservice package (Xiao, 2018), a sentence encoding service for mapping variable-length sentences to fixed-length vectors.

Original:  BERT-Base-Uncased isused for CB12 , and BERT-Base-MultilingualCased (New) for Randstad .
Revised: We default to using the pre-trained BERT models provided by the package, i.e., BERT-Base-Uncased is used for CB12 , and BERT-Base-Multilingual-Cased (New) for Randstad .

Original: A. Parameter Settings
Revised: A. Parameter Settings

Original: Our implementation is based on the PyTorch version of the DGL package (Wang et al., 2019a).
Revised: Our implementation is based on the PyTorch version of the DGL package (Wang et al., 2019a).

Original: For job title classification , we randomly split the5 https://code.google.com/archive/p/ word2vec/ data into training/validation/test sets with a ratio of 60%/20%/20%.
Revised: For job title classification , we randomly split the data into training/validation/test sets with a ratio of 60%/20%/20%.

Original: We keep the same split ratio on positive/negative edges for next-job prediction , where negative edges are randomly picked from unconnected node pairs (i.e., the same size as positive edges).
Revised: We keep the same split ratio on positive/negative edges for next-job prediction , where negative edges are randomly picked from unconnected node pairs (i.e., the same size as positive edges).

Original: To ensure fairness, we keep the same data split for  both methods.
Revised: To ensure fairness, we keep the same data split for all methods, and we set the node embedding to 128 for all methods, except for W2V . In the job title classification and next-job prediction tasks, for unsupervised methods, node representations are learned from the entire dataset. The logistic regression classifier is then trained on both the training and validation sets.

Original: Each semi-supervised model was trained on the training set, and the parameters were optimized on the validation set.
Revised: Each semisupervised model is trained on the training set, and the parameters are optimized on the validation set.

Original: The final performance was evaluated on the test set.
Revised: The final performance is evaluated on the test set.

Original: Models are optimized with the Adam (Kingma and
Revised: Models are optimized with the Adam (Kingma and

Original: Ba, 2014) with a learning rate of 1e-3, and we apply
Revised: Ba, 2015) with a learning rate of 1e-3, and we apply

Original: L 2 regularization with value 5e-4.
Revised: L 2 regularization with value 5e-4.

Original: We use an early stop with a patience of 100, i.e., if the validation loss does not decrease in 100 consecutive epochs, we stop training.
Revised: We use an early stop with a patience of 100, i.e., if the validation loss does not decrease in 100 consecutive epochs, we stop training.

Original: For models applying the attention mechanism, the dropout rate of attention is set to 0.2.
Revised: For models applying the attention mechanism, the dropout rate of attention is set to 0.2.

Original: For random-walk-based methods include N2V and M2V , we set the window size to 5, walk length to 10, walks per node to 50, the number of negative samples to 5.
Revised: For random-walk-based methods, including N2V and M2V , we set the window size to 5, walk length to 10, walks per node to 50, the number of negative samples to 5.

Original: For M2V , we test all meta-paths and report the best performance.
Revised: For M2V , we test all metapaths and report the best performance.

Original: For a fair comparison, we set the dimension of node embedding to 128 for all the above methods, except for W2V .

Original: In both job title classification and next-job prediction , for unsupervised methods, node representations are learned from the entire dataset.

Original: The logistic regression classifier is then trained on both the training and validation sets.

Original: Edge features are represented by applying binary operators (Grover and Leskovec, 2016) on pairs of nodes. We use dot product for all semi-supervised methods, and for unsupervised methods, we choose the best operator  based on the validation set.
Revised: For next-job prediction task, edge features are represented by applying binary operators (Grover and Leskovec, 2016) on pairs of nodes, and then the best operator is chosen based on the validation set, while the dot product is used for all semi-supervised methods.

Original: We repeat each prediction experiment ten times and report the average performance scores (i.e., Macro-F1 and Micro-F1 for job  classification and AUC for next-job prediction).
Revised: We repeat each prediction experiment ten times and report the average performance scores (i.e., Macro-F1 and Micro-F1 for job title classification and AUC for next-job prediction ).