HR-Specific NLP for the Homogeneous Classification of Declared and Inferred Skills

Figures & data

Figure 1. Functional flow of the algorithmic framework.

Algorithm 1. Stream A (PoS) pseudocode for time instant t.

1: Map input document $u\left(t\right)=\left[u_1,.\dots ,\hspace{0.17em}{u}_w\right]$, where $w\in \left|w\right.⟩j$ is the cardinality of the input document, to PoS tag vector $y\left(t\right):=\left[Po{S}_1,.\dots ,\hspace{0.17em}Po{S}_w\right]$ using the PoS tagging fine-tuned BERT model.

2: Apply grammar rules 1), 2) and 3) to PoS tag vector $y\left(t\right)$ for skill extraction 3: Return candidate skill vector $x_t$ associated with input document $u\left(t\right)$ 4: Apply exact matching on extracted skills in $x_t$ 5: Apply fuzzy matching on extracted skills in $x_t$ 6: Return homogenized skill vector $r_t$ (associated with $x_t$) using only entries from $\mathcal{D}\left(t\right)$ 7: Update $\mathcal{D}\left(t\right)$ with feedback from newly discovered skills

Algorithm 2. Stream B (TF-IDF) pseudocode for time instant t.

1: Encode the reference skills appearing in the reference skill dictionary $\mathcal{D}\left(t\right)$ via TF-IDF based n-gram vectorization according to the procedure described in Singh and Sathi,).

2: Encode the generic skill phrase $x_{ij}$ in document $x_t$ via TF-IDF based n-gram vectorization according to the same procedure as in ln. 1.

3: Compute efficient cosine similarity between computed n-grams according to Python package sparse_dot_topn (2022) and return only the skill phrases that are assigned the highest similarity score (above a given threshold $T_B$).

Algorithm 3. Stream C (W2V) pseudocode for time instant t.

1: Compute the word embedding reference skills appearing in the reference skill dictionary $\mathcal{D}\left(t\right)$ using the pre-trained version of the BERT encoder in (https://colab.research.google.com/drive/1ZQvuAVwA3IjybezQOXnrXMGAnMyZRuPU, BERT, 2022.

2: Compute the word embedding of the generic skill phrase $x_{ij}$ in document $x_t$ using the same pre-trained version of the BERT encoder as in ln. 1.

3: Compute standard cross-correlation between the word embeddings computed at ln. 1–2.

4: Return as candidate skill set $SC$ only the skills in the reference dictionary that score the highest similarity (namely, above the threshold $T_C$).

Algorithm 4. Stream C (W2V) pseudocode for time instant t.

Given $C{V}_i$ of employee $i$, apply NER control action $a\left(t\right)$ so that $X\left(t\right)\to R\left(t\right)$.

The NER control action $a\left(t\right)$ is a sequence of parallelized instructions (namely, lines 1–3 below) as follows:

1: Stream A pseudocode for time instant $t$ (PoS)

2: Stream B pseudocode for time instant $t$ (TF-IDF)

3: Stream C pseudocode for time instant $t$ (W2 V)

4: Then, compute final similarity score $s_{p,q},\mathrm{\forall }p,q$ relevant to document $t$ in $C{V}_i$ (i.e., associated with the $t$-th row of $X\left(t\right)$)

5: Select only the the first $k\cdot N$ skill phrases $x_p$ such that $s_{p,q}>\mu \cdot s_{max}$ and discard all other skill phrases.

6: For the selected $x_p$’s, replace $x_p\leftarrow x_q^{ref}.$

Algorithm 5. Skill inference pseudocode (BERToby for hobbies and JoBERT for work experience).

1: Preprocessing input through BERT embeddings in order to identify and restrict the inference domain. 2: The fine-tuned fully-connected layers appended to the BERT model allow to classify the embedded input. 3: The skills associated to the predicted class are inferred.

Figure 2. Fine-tuning BERT in order to obtain BERToby.

Figure 3. Fine-tuning BERT in order to obtain JoBERT.

Table 1. Accuracy performance for 400 candidate profiles at 1^st, 3^rd and 5^th recommendation, respectively.

Algorithm	@1	@3	@ 5
Semantic skill matching (NER)	0.89	0.98	1
JD matching (Gugnani and Misra (2020))	0.84	0.95	0.98

Figure 4. Algorithm runtime [ms] vs. dimensionality of $\mathcal{D}\left(t\right)$.

Table 2. Bertoby performance metrics.

Class	Precision	Recall	F1-score
Art & craft	1	0.92	0.96
Collecting	1	0.84	0.91
Extreme sports	0.88	0.88	0.88
Food & drinks	0.89	1	0.94
Games	0.92	0.96	0.94
Individual sports	0.88	0.92	0.90
Model & electronics	0.96	1	0.98
Music	1	1	1
Performing arts	0.96	0.96	0.96
Pets	1	1	1
Spiritual & mental	0.96	0.92	0.94
Team sports	0.96	1	0.98
Accuracy			0.95
Macro average	0.95	0.95	0.95
Weighted average	0.95	0.95	0.95

Table 3. JoBERT performance metrics.

Class	Precision	Recall	F1-score
Administrative and commercial managers	0.74	0.96	0.83
Assemblers	0.87	1	0.93
Building and related trade workers	0.96	0.92	0.94
Business and administration associate professionals	0.89	0.68	0.78
Business and administration professionals	0.79	0.78	0.78
Chief executives, senior officials and legislators	1	1	1
Customer service clerks	0.96	1	0.98
Drivers and mobile plant operators	0.92	0.89	0.91
Electrical and electronics trade workers	0.86	0.96	0.91
Food processing, woodworking, garment and other craft and related trade workers	0.80	0.74	0.77
Handicraft and printing workers	0.96	0.89	0.92
Health associate professionals	0.88	0.78	0.82
Hospitality, retail and other service managers	0.93	0.96	0.94
Information and communication technicians	0.96	1	0.98
Labourers in mining, construction, manufacturing and transport	0.93	1	0.96
Legal, social and cultural prfessionals	0.77	0.77	0.77
Market-oriented skilled agricultural workers	0.96	1	0.98
Numerical and material recording clerks	0.96	0.96	0.96
Personal service workers	0.92	0.85	0.88
Production and specialized service managers	0.79	0.88	0.84
Protective service workers	0.89	0.96	0.93
Sales workers	1	1	1
Science and engineering associate professionals	0.84	0.74	0.78
Science and engineering professionals	0.88	0.81	0.79
Stationary plant and machine operators	0.78	0.81	0.79
Teaching professionals	0.96	0.96	0.96
Accuracy			0.88
Macro average	0.88	0.88	0.88
Weighted average	0.88	0.88	0.88

BERT encoder (pre-trained), https://colab.research.google.com/drive/1ZQvuAVwA3IjybezQOXnrXMGAnMyZRuPU, last accessed on June 8th , 2022.

 Google Scholar

Gugnani, A., and H. Misra, Implicit skills extraction using document embedding and its use in job recommendation. In Proceedings of the 32ndInnovative Applications of Artificial Intelligence Conference (IAAI-20) New York Hilton Midtown, New York, New York, USA, 2020, 34( 08), 13286–93.

 Google Scholar