# Exploring Historical Labor Markets: Computational Approaches to Job Title Extraction

Raven Adam¹, Klara Venglarova¹, Georg Vogeler¹

¹University of Graz, Graz, Austria

Corresponding author: Raven Adam, raven.adam@uni-graz.at

## Abstract

Historical job advertisements provide invaluable insights into the evolution of labor markets and societal dynamics. However, extracting structured information, such as job titles, from these OCRed and unstructured texts presents significant challenges. This study evaluates four distinct computational approaches for job title extraction: a dictionary-based method, a rule-based approach leveraging linguistic patterns, a Named Entity Recognition (NER) model fine-tuned on historical data, and a text generation model designed to rewrite advertisements into structured lists.

Our analysis spans multiple versions of the ANNO dataset, including raw OCR, automatically post-corrected, and human-corrected text, as well as an external dataset of German historical job advertisements. Results demonstrate that the NER approach consistently outperforms other methods, showcasing robustness to OCR errors and variability in text quality. The text generation approach performs well on high-quality data but exhibits greater sensitivity to OCR-induced noise. While the rule-based method is less effective overall, it performs relatively well for ambiguous entities. The dictionary-based approach, though limited in precision, remains stable across datasets.

This study highlights the impact of text quality on extraction performance and underscores the need for adaptable, generalizable methods. Future work should focus on integrating hybrid approaches, expanding annotated datasets, and improving OCR correction techniques to enhance the extraction of structured information from historical texts. These advancements will enable deeper exploration of labor market trends and contribute to the broader field of digital humanities.

## Keywords

historical newspapers; job advertisements; job title extraction; NER; occupations dictionary; text-generation; OCR quality effect

## I INTRODUCTION

Job advertisements from historical newspapers contain valuable information about history and development of the labor market. However, analyzing text of the advertisements that was turned into machine-readable form through the OCR process (OCRed text [Chiron et al., 2017b]) brings along the challenge of defining their structure and automatically extracting its components, in contrast to digitally-born data. Building on our previous work [Venglarova et al., 2024], we focus on extracting job titles from unstructured historical job advertisements in this paper.

While research concerning modern job advertisements profits from their structure, such as HTML tags, and works with already identified job titles [Colace et al., 2019, Boselli et al.,
2017, Zhu et al., 2017], we face the challenge of their definition and automatic extraction. We
are comparing several methods for this task.

In comparison with our previous work [Venglarova et al., 2024], we add other models to our
evaluation which reach even better performance for the job title extraction task. In addition, we
are including an evaluation on an external dataset, to assess the robustness and generalization
capacity of the methods used. This is important as we should aim for more general methods
and not only corpus-specific ones. While the data in our previous work were manually corrected
and cleaned, the real-life applications often include working with non-human-corrected output,
whether an automatically post-corrected text or directly a raw OCR output. To account for the
text quality influence, we are adding a comparison of our methods' performance on raw OCR,
automatically post-corrected and human corrected text.

## II RELATED WORK

Modern research on job advertisements often benefits from their structured format and therefore
the number of research works that identify job titles is limited. This task was addressed, for in-
stance, by [Bandara et al., 2021] who used rule-based methods to extract job titles from OCRed
text, however, with a limited accuracy of 56%. Other approaches include cleaning job titles us-
ing a manually created to-delete list [Rahhal et al., 2023] or normalizing job titles manually or
semi-automatically [Neculoiu et al., 2016], sometimes while relying on an external taxonomy.
Since none of these methods is suitable for our use case, we explore approaches for information
extraction.

Named entity recognition (NER) has been widely applied to historical data in previous research
[Grover et al., 2008, Ehrmann et al., 2016, Won et al., 2018, Labusch et al., 2019], typically
extracting standard entities such as person and location names from historical texts. Although
job titles do not belong among standard entities, the results of NER on historical data in general
encourages us to develop a custom NER pipeline specifically for extracting job titles.

With the emergence of large language models (LLMs), the NER can also be approached as a
text generation or translation task [Keraghel et al., 2024]. To our knowledge, this approach has
not yet been tested on historical data; however, it has shown promising results across various
domains [Tavan and Najafi, 2022, Wang et al., 2023]. For a more detailed overview of related
work in all above-mentioned areas, we point readers to the conference paper [Venglarova et al.,
2024].

Building on this foundation, our paper compares several extraction methods, but also intro-
duces their evaluation on an external dataset that was OCRed but not human-corrected, which
may impact the performance of our extraction methods. This exploration complements studies
like [Rodriguez et al., 2012], who evaluated several NER tools for identifying persons, loca-
tions and organizations on two datasets containing raw and manually corrected OCR output.
The OCRed text's average word accuracy reached 88.6% and average character accuracy was
93% [Rodriguez et al., 2012]. They conclude that correcting OCRed text did not significantly
improve entity recognition or NER tool performance for a certain entity type.

Strien et al. [2020] explored the impact of OCR errors on several tasks common in digital
humanities, including NER. Using a corpus of digitized newspapers, they grouped articles into
different OCR quality bands. Their findings showed that while NER performance varied across
quality bands and entities, OCR errors had a smaller impact on the NER than on tasks such as
sentence segmentation or dependency parsing [Strien et al., 2020]. For example, the average F-
<table><caption>Table 1: Quality of raw OCR output and automatically post-corrected text for ANNO corpus. Human-corrected text served as a ground truth.</caption><thead><tr><th></th><th>CER</th><th>WER</th><th>BLEU</th></tr></thead><tbody><tr><td>Raw OCR</td><td>0.0781</td><td>0.1972</td><td>74.94</td></tr><tr><td>Automatically Post-corrected Text</td><td>0.0727</td><td>0.1589</td><td>79.62</td></tr></tbody></table>

<table><caption>Table 2: Example of an advertisement text as a raw OCR output, automatically post-corrected and human-corrected text. [Hardworking carpenter will be accepted. Wilflingseder wood-working factory, Ried im Innkreis. 135]. The original image available from Linzen Volks-blatt, 8.2.1922, p. 6, https://anno.onb.ac.at/cgi-content/annoshow?call=lvb|19220208|006|100 [cited 12.3.2025]</caption><thead><tr><th></th><th>Original</th></tr></thead><tbody><tr><td>Raw OCR</td><td>Tu chtiger Ti chler wird aufgenommen. Holzbearbei tungsfabrit Wiiflingseder, 1155 im Innkreis. 5</td></tr><tr><td>Automatically Post-corrected Text</td><td>Tüchtiger Tischler wird aufgenommen. Holzbearbeitungsfabrik Willingseder, 1155 im Innkreis. 5</td></tr><tr><td>Human-Corrected Text</td><td>Tüchtiger Tischler wird aufgenommen. Holzbearbeitungsfabrik Wilflingseder, Ried im Innkreis. 135</td></tr></tbody></table>

score for recognizing person entities ranged from 0.87 for the quality band 1 (≥ 0.9 Levenshtein similarity between OCRed text and its ground truth) to 0.63 for the quality bound 4 (≤ 0.7 Levenshtein similarity).

Both studies highlight that recognition results vary across different types of entities. Since job titles are not standard entities, it remains an open question how OCR errors specifically affect their extraction.

### III DATASET

Our experiments and evaluation have been conducted on 14 different digitized newspapers from the ANNO corpus [Österreichische Nationalbibliothek, 2021] from 1850-1950 (Fig. 1). From them, we used a subset of job advertisements that we manually annotated, OCRed and manually corrected, and afterwards annotated all job titles within them using the doccano software [Nakayama et al., 2018]. The resulting dataset consisted of 1,486 job advertisements as training data and 637 as testing data. The training and testing dataset are mutually exclusive and were splitted randomly. For more details and examples, we point readers to [Venglarova et al., 2024]. We use the dataset in three versions: as raw OCRed output, as automatically post-corrected text and as human-corrected data. Tab. 1 summarizes their text quality and Tab. 2 shows an example of an advertisement in these three different text-quality settings.

Three different metrics were used to measure text quality. Character error rate (CER) represents the proportion of incorrect characters, due to substitutions, insertions, or deletions, relative to the total number of characters in the correct reference text. A lower CER indicates better performance, with a CER of zero meaning that the recognized text perfectly matches the reference.
Word error rate (WER) measures the proportion of incorrect words in the recognized text compared to the reference text, considering substitutions, insertions, and deletions. Like CER, a lower WER indicates better accuracy, with a WER of zero meaning a perfect match.

Bilingual Evaluation Understudy (BLEU) is a metric used to evaluate the quality of machine-generated text by comparing it to one or more reference texts. It measures how many words or phrases in the generated text match those in the reference, considering both precision and fluency. BLEU assigns higher scores to outputs that closely resemble the reference in wording and structure, with a maximum score of 100 indicating a perfect match.

<figure><img src="image_1.png" /><figcaption>Figure 1: An example page from the ANNO corpus (left) and DDB (right) containing job advertisements. Left image: Grazer Tagblatt, 19.2.1914, p. 14. https://anno.onb.ac.at/cgi-content/annoshow?call=gtb|19140219|14|100.0|0 [cited 11.3.2025]. Right image: Hamburger Fremdenblatt, 6.3.1914, p. 33. https://www.deutsche-digitale-bibliothek.de/newspaper/item/2BZKW5C4ERCLHL7VFCO3BFISZEPJZLFR?issuepage=33 [cited 13.3.2025]</figcaption></figure>

Aiming to assess the impact of text quality and OCR errors on the performance of the different extraction techniques, evaluations were conducted using raw OCR text, automatically corrected text, and human-corrected text. Since the annotated job entities were only available for the human-corrected text, direct matching across the three versions was challenging due to inconsistencies introduced by OCR errors.

To address this, fuzzy matching based on a Levenshtein similarity threshold of 0.6 was employed. This approach allowed each job position in the human-corrected text to be matched to its most likely candidate in the OCR and automatically corrected text. Despite this effort, severe
OCR errors in some cases rendered matching impossible (Fig. 2), leading to a reduction in the dataset size. The final dataset comprised 1882 job advertisements, ensuring that job positions were available across all three text versions for consistent evaluation.

```json
: { 'text': 'Ein 25747 Honemiis, nach der Auslehre und ein gesit teter Knabe .* aufgenommen bei Simon Fei ,„Manufaktur und Möbelg 2— am Smichov.,',
   'entities': ['Kommis']}
```

<caption>Figure 2: Example of severe OCR errors making the match impossible. The similarity based on Levenshtein distance between *Kommis [clerc]* and *Honemiis* is 0.5 and thus under the similarity threshold</caption>

To gain insights about the generalization abilities of the models, we decided to test them also on an external dataset, which was not part of the training nor testing data. For this, we used a subset of a collection of digitized newspapers provided by the German Digital Library (DDB). The entire dataset covers the years 1914 to 1945 but the subset used in this work is from the year 1914 only. As this dataset is fairly new, no ground truth data is currently available and measures for OCR quality such as WER or CER cannot be calculated. After several preprocessing steps to retrieve relevant pages and manual annotation in doccano, the final dataset consisted of 377 job ads. Before the annotation, the raw ocr text was also corrected with a ByT5 model [Löfgren and Dannélls, 2024], fine-tuned on the human-corrected ANNO corpus and the ICDAR2019 dataset.

# IV METHODS

## 4.1 Annotation Process

The first step in annotating job titles within our corpora was to set annotation guidelines. In the ANNO dataset, the job titles were annotated as *positions*, if they explicitly mentioned a position (e.g., *Buchhalter [accountant]*, *Köchin [cook, f.]* ) and did not contain any spelling or spacing errors. If errors appeared in the position name, the whole advertisement was excluded from the evaluation. The training dataset includes 1962 annotated positions, while the testing dataset comprises 849 annotated positions.

To the DDB dataset, the same rules could not be applied, as the OCR quality was so low that leaving out all the positions containing errors would leave but a very little sample. Also, including positions with errors is more similar to real-life applications, as in reality the large amounts of text can hardly be manually corrected. For this reason, we included all positions that a human annotator could still identify as a position and make a sense of its closest surroundings. That includes also positions with spelling and spacing mistakes (Fig. 3).

Reifende für neue Zeitschrift nach auswärts gesucht. Hoher Verdienst. 10–1 Uhr. Atlas=Verlag, Oranienstraße 110.
•position

<caption>Figure 3: Example of an error in annotated positions in the DDB dataset. *Reifende* should be *Reisende* (with a long s). [Travel agent for new newspaper wanted. High earnings. 10-1 o’clock. Atlas=Verlag, Oranienstraße 110.] Duccano interface.</caption>

When analyzing preliminary results from the ANNO dataset, we discovered that some models also tended to identify nouns like *woman* as positions in sentences like *A young woman is looking for washing and ironing work*. This led us to reconsider the definition of a job title and introduce a new label *ambiguous* for the DDB dataset to enable us to measure how models identify these nouns while being able to distinguish them from the “standard” positions. This dataset thus comprised 493 *position* entities and 90 *ambiguous* entities (Fig. 4).
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /><figcaption>Figure 4: Example of a "standard" (left) and "ambiguous" (right) job title. Left: [Hardworking saleswoman in the delicatessen and food branch, looks for a position in the local area. Please write to H. B., Petersgasse 58, 1st floor left. 1983B]. Right: [Young man with 4-year reference, nice handwriting, good calculator, is looking for a job. Please write to Hans Wotke, Graz VI, Draisgasse 18, 1st floor. 1030]. Examples from Grazer Tagblatt, 3.18.1914, p. 12, https://anno.onb.ac.at/cgi-content/annoshow?call=gtb|19140318|12|100.0|0 [cited 11.3.2025].</figcaption></figure>

## 4.2 Post-Correction

Extracting high-quality OCR from historical texts remains a significant challenge, particularly in advertisement sections, which often feature complex layouts and non-standard formatting [Wevers, 2022]. As a result, research has increasingly focused on improving and correcting OCR output using Natural Language Processing (NLP) techniques [Chiron et al., 2017a, Rigaud et al., 2019]. However, the impact of OCR post-correction on downstream tasks, such as NER, remains insufficiently understood, with studies reporting mixed results [Ehrmann et al., 2024].

To assess whether OCR post-correction can enhance job position extraction, we fine-tuned the hmByT5 model on 80% of our human-corrected text data and the DE1, DE2, DE3 and DE7 parts of the ICDAR2019-POCR dataset [Rigaud et al., 2019]. Recent studies indicate that ByT5 variants are particularly well suited for OCR post-correction due to their ability to process text at the character level, which allows for fine-grained corrections [Löfgren and Dannélls, 2024, Debaene et al., 2025, Guan and Greene, 2024].

## 4.3 Extraction methods

Four distinct techniques were employed to extract job position titles from the advertisements, each leveraging different linguistic and computational methodologies:

* **Dictionary-Based Approach:** A dictionary-based approach was implemented using data from the Historical International Standard Classification of Occupations (HISCO) [Leeuwen et al., 2002]. A dictionary of job titles was constructed by extracting position names and their variants from the HISCO dataset. Job advertisements were then matched against this dictionary, and any exact or approximate matches were identified as job position titles. This approach emphasizes historical relevance and semantic alignment with the HISCO dataset.

* **Rule-Based Approach:** A rule-based method was developed using Part-of-Speech (POS) tagging and linguistic structure analysis. Advertisements were tokenized, and POS tags were assigned to each token using the spaCy 'de_core_news_lg' model [Honnibal and Montani, 2018]. Rules were crafted based on common linguistic patterns observed in job titles. A detailed description of these rules can be found in the previous work [Venglarova et al., 2024] and is also available as code on GitHub (see Code and Data Availability section).
* **Named Entity Recognition (NER) Approach:** A Named Entity Recognition-inspired approach was implemented using the spaCy library to fine-tune 'zeitungs-lm-v1', an Electra model. The model was pre-trained on historical text corpora to capture linguistic nuances specific to the time period. Annotated job advertisements, where job titles were labeled as entities, were used for its fine-tuning. This approach leverages modern transformer-based techniques to detect job titles with high contextual accuracy.

* **Text Generation Approach:** HmByT5, a ByT5 text generation model pre-trained on historical data, was employed to rewrite job advertisements into a structured list of job positions. A curated dataset of historical job advertisements was used to learn the transformation from free-form text to a structured list of positions. The output was then post-processed to ensure comparability with the other approaches.

## 4.4 Evaluation

Each extraction technique was evaluated using the following metrics:

* **Precision:** The proportion of correctly identified job titles out of all identified titles.

* **Recall:** The proportion of correctly identified job titles out of all actual job titles in the dataset.

* **F1 Score:** The harmonic mean of precision and recall, providing a balanced measure of performance.

Additionally, to assess the generalizability of the techniques, all approaches were also evaluated on the unrelated DDB dataset. This evaluation aimed to determine the robustness of each method when applied to a different linguistic and contextual setting.

# V RESULTS

In this section, we present evaluation results for several approaches on different tasks. Table 3 shows the comparison of results of different extraction methods for the raw OCR, automatically post-corrected, and human corrected text from ANNO corpus. We include the evaluation on the training data as an estimation of the difficulty of the task and evaluation on the testing data that the model has not seen during the training process. In the table, the F1-score is presented. The full results containing recall and precision can be found in the Appendix A.

<table><caption>Table 3: F1-scores for position extraction methods on the texts of different quality.</caption><thead><tr><th rowspan="2">Method</th><th colspan="2">Raw OCR</th><th colspan="2">Automatically Post-corrected Text</th><th colspan="2">Human-corrected Text</th></tr><tr><th>Train</th><th>Test</th><th>Train</th><th>Test</th><th>Train</th><th>Test</th></tr></thead><tbody><tr><td>Dictionary -based</td><td>0.517</td><td>0.509</td><td>0.554</td><td>0.552</td><td>0.589</td><td>0.582</td></tr><tr><td>Dictionary -based with Tolerance</td><td>0.385</td><td>0.379</td><td>0.395</td><td>0.397</td><td>-</td><td>-</td></tr><tr><td>Rule-based</td><td>0.668</td><td>0.65</td><td>0.681</td><td>0.722</td><td>0.743</td><td>0.714</td></tr><tr><td>NER</td><td><strong>0.882</strong></td><td><strong>0.855</strong></td><td><strong>0.942</strong></td><td><strong>0.907</strong></td><td><strong>0.996</strong></td><td><strong>0.956</strong></td></tr><tr><td>Text Generation</td><td>0.802</td><td>0.754</td><td>0.829</td><td>0.775</td><td>0.990</td><td>0.922</td></tr></tbody></table>

Table 4 shows the results of job titles extraction on an external dataset that was used to assess
our methods' robustness and ability to generalize. The results are presented separately for all annotated entities. In our dataset, 493 'position' entities and 90 'ambiguous' entities are present. In the table, the F1-score is shown. The full results containing recall and precision can be found in the Appendix B. The training and testing datasets are the same for all versions.

<table><caption>Table 4: F1-scores for position extraction methods on the DDB non-human-corrected dataset.</caption><thead><tr><th>Method</th><th>All Entities</th><th>Position Entities</th><th>Ambiguous Entities</th></tr></thead><tbody><tr><td>Dictionary-based</td><td>0.511</td><td>0.562</td><td>0</td></tr><tr><td>Rule-based</td><td>0.472</td><td>0.466</td><td><strong>0.098</strong></td></tr><tr><td>NER</td><td><strong>0.807</strong></td><td><strong>0.835</strong></td><td>0.06</td></tr><tr><td>Text Generation</td><td>0.705</td><td>0.766</td><td>0.074</td></tr></tbody></table>

## VI DISCUSSION

The performance of all methods remains in consistent order across texts of different qualities, with the lowest performance of the dictionary-based approach and the highest of the custom NER method. All methods perform better with increasing text quality. For the text generation and the NER methods, the results on the training dataset are consistently better than on the testing dataset, which is an expected behaviour.

For the dictionary-based method, a tolerance was introduced through Levenshtein similarity of 0.8. The assumption was that if the lower-quality text contains OCR mistakes, a tolerance would help to perform the matches between the dictionary and job titles within the advertisements. However, the results with the tolerance are worse rather than better. Searching for a cause, we found a number of false-positives introduced by this approach:

> *Werkschutz-Wachmänner sucht grötzeres Unternehmen. Bewerbungen mit Lebenslauf, Gehalt-sansprüchen und fruihestem Eintrittstermin unter "Werkschutz ND" an den Verlag.*

> *[Plant security guards sought by a larger company. Applications with CV, salary expectations and earliest starting date under 'Werkschutz ND' to the publisher.]*

> *Annotations: Werkschutz-Wachmänner [plant security guards]*

> *Predictions: Unternehmen [company]*

In this example, the Levenshtein distance allowed for *Unternehmen [company]* to be identified as a job title, because a businessman [*Unternehmer*] is listed in the dictionary.

Despite a decline in performance with lower text quality, the NER approach demonstrated relatively stable results across all three versions of the ANNO dataset. This is in line with previous research showing that transformer based NER is relatively stable across different OCR mistake severities [Strien et al., 2020, Hu et al., 2021]. This consistency of the NER approach can be especially helpful when creating a labeled dataset, as using human-corrected or even automatically corrected text will be easier for annotators than using raw OCR text. In contrast, the performance of the text generation approach notably decreases when used on text with lower quality than it was trained on.

For the DDB dataset, the methods perform in the same order of success as for the ANNO data. Most of the approaches reach lower scores than on the raw OCR ANNO data, which could be however expected due to lower OCR quality of the DDB. Another factor to consider is the possibility of some structural differences between job advertisements in Austrian and German
newspapers. In this dataset, both ‘position’ and ‘ambiguous’ entities were annotated. In most
cases, the performance is very low for the ambiguous entities, which was expected, as none of
the models was specifically trained on them.

While the dictionary-based approach reached the lowest performance, it stays stable across the datasets, as the results for the raw OCR in the ANNO corpus and the DDB dataset are comparable. This welcome behaviour is probably due to the choice of the dictionary, as the HISCO database is not specific to either of our corpora. Potential constraints for further application of this approach to other corpora could be e.g. language variations across time and space.

The rule-based approach had a significant drop of performance for the DDB dataset for the all entities task. However, it has the highest performance for the ambiguous entities from all the approaches. This can be explained by the consistency of the linguistic structure of position and ambiguous entities, even if the rules were built for the position entities only. Extending this result to the NER and text generation approaches, it appears that these methods may place less emphasis on the linguistic structure when developing their representation models. If they did, the identification of ambiguous entities would likely be more consistent. Instead, they appear to focus more on the semantic level, which makes identifying ambiguous entities that also appear in many other contexts much more difficult. This is supported by previous research showcasing that transformer based models excel at semantic representations but show mixed performance when it comes to structural understanding [Pavlick, 2022, Rogers et al., 2020].

VII CONCLUSION

This study highlights the challenges and opportunities in extracting job titles from historical
job advertisements using various computational methods. Our findings underscore the impor-
tance of considering text quality, linguistic structure, and dataset variability when designing and
evaluating extraction techniques.

The NER approach consistently outperformed other methods, demonstrating robustness across text quality variations and datasets. Its ability to learn contextual and semantic representations made it particularly effective and stable, even in the presence of OCR errors. However, its performance on ambiguous entities remains a limitation, suggesting the need for additional training data or model adaptations to better handle such cases. The text generation approach showed promise, particularly on high-quality data, but exhibited greater sensitivity to OCR errors, indicating potential areas for improvement in handling noisy input.

The rule-based approach, while less effective overall, performed relatively better for ambiguous entities than the other approaches, likely due to its reliance on consistent linguistic patterns. This suggests that integrating rule-based heuristics with more advanced models could improve overall performance. The dictionary-based method, despite its limitations, demonstrated stability across datasets, highlighting the utility of domain-specific resources like HISCO for historical data analysis.

Our evaluation on the external DDB dataset revealed the generalization challenges faced by all
methods, particularly due to differences in OCR quality and linguistic characteristics. Nonethe-
less, we were also able to showcase that the trained models generalize reasonably well even to
unrelated and low OCR quality data and can help to provide valuable insights into labor market
trends and societal changes over time.
# VIII ACKNOWLEDGMENTS

We would like to thank the Austrian National Library (ÖNB) for providing the data, Saranya Balasubramanian for a great help with its processing, and Wiltrud Mölzer, Jörn Kleinert, Meike Linnewedel, Clara Hochreiter and Melanie Frauendorfer for their efforts in correcting and annotating it. This work was supported by the FWF under grant number P 35783.

# IX CODE AND DATA AVAILABILITY

The code and data containing advertisements text and annotated positions, are available at
https://github.com/JobAds-FWFProject/PositionsExtraction.

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A APPENDIX RESULTS FOR ANNO DATASET

<table><caption>Table 5: F1-score, recall and precision for different methods for the task of job titles extraction on the raw OCR text from the ANNO dataset.</caption><thead><tr><th rowspan="3">Method</th><th colspan="6">Raw OCR</th></tr><tr><th colspan="2">F1 Score</th><th colspan="2">Recall</th><th colspan="2">Precision</th></tr><tr><th>Train</th><th>Test</th><th>Train</th><th>Test</th><th>Train</th><th>Test</th></tr></thead><tbody><tr><td>Dictionary -based</td><td>0.517</td><td>0.509</td><td>0.521</td><td>0.538</td><td>0.513</td><td>0.484</td></tr><tr><td>Dictionary -based with Tolerance</td><td>0.385</td><td>0.379</td><td>0.367</td><td>0.372</td><td>0.406</td><td>0.387</td></tr><tr><td>Rule-based</td><td>0.668</td><td>0.65</td><td>0.674</td><td>0.653</td><td>0.661</td><td>0.647</td></tr><tr><td>NER</td><td><strong>0.882</strong></td><td><strong>0.855</strong></td><td><strong>0.815</strong></td><td><strong>0.787</strong></td><td><strong>0.961</strong></td><td><strong>0.936</strong></td></tr><tr><td>Text Generation</td><td>0.802</td><td>0.754</td><td>0.793</td><td>0.739</td><td>0.810</td><td>0.769</td></tr></tbody></table>

<table><caption>Table 6: F1-score, recall and precision for different methods for the task of job titles extraction on the automatically post-corrected text from the ANNO dataset.</caption><thead><tr><th rowspan="3">Method</th><th colspan="6">Automatically Post-corrected Text</th></tr><tr><th colspan="2">F1 Score</th><th colspan="2">Recall</th><th colspan="2">Precision</th></tr><tr><th>Train</th><th>Test</th><th>Train</th><th>Test</th><th>Train</th><th>Test</th></tr></thead><tbody><tr><td>Dictionary -based</td><td>0.554</td><td>0.552</td><td>0.606</td><td>0.629</td><td>0.51</td><td>0.492</td></tr><tr><td>Dictionary -based with Tolerance</td><td>0.395</td><td>0.397</td><td>0.393</td><td>0.408</td><td>0.398</td><td>0.387</td></tr><tr><td>Rule-based</td><td>0.681</td><td>0.722</td><td>0.612</td><td>0.731</td><td>0.769</td><td>0.713</td></tr><tr><td>NER</td><td><strong>0.942</strong></td><td><strong>0.907</strong></td><td><strong>0.911</strong></td><td><strong>0.866</strong></td><td><strong>0.975</strong></td><td><strong>0.953</strong></td></tr><tr><td>Text Generation</td><td>0.829</td><td>0.775</td><td>0.829</td><td>0.769</td><td>0.829</td><td>0.781</td></tr></tbody></table>
<table><caption>Table 7: F1-score, recall and precision for different methods for the task of job titles extraction on the human-corrected text from the ANNO dataset.</caption><thead><tr><th rowspan="2">Method</th><th colspan="6">Human-corrected Text</th></tr><tr><th colspan="2">F1 Score</th><th colspan="2">Recall</th><th colspan="2">Precision</th></tr><tr><th></th><th>Train</th><th>Test</th><th>Train</th><th>Test</th><th>Train</th><th>Test</th></tr></thead><tbody><tr><td>Dictionary-based</td><td>0.589</td><td>0.582</td><td>0.454</td><td>0.446</td><td>0.837</td><td>0.838</td></tr><tr><td>Dictionary-based with Tolerance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Rule-based</td><td>0.743</td><td>0.714</td><td>0.754</td><td>0.721</td><td>0.733</td><td>0.708</td></tr><tr><td>NER</td><td><strong>0.996</strong></td><td><strong>0.956</strong></td><td><strong>0.995</strong></td><td><strong>0.939</strong></td><td><strong>0.997</strong></td><td><strong>0.973</strong></td></tr><tr><td>Text Generation</td><td></td><td>0.922</td><td></td><td>0.913</td><td></td><td>0.932</td></tr></tbody></table>

## B APPENDIX RESULTS FOR DDB DATASET

<table><caption>Table 8: F1-score, recall and precision for different methods for the task of job titles extraction from the DDB dataset. Results for both labels ‘position’ and ‘ambiguous’.</caption><thead><tr><th>Method</th><th>F1 Score</th><th>Recall</th><th>Precision</th></tr></thead><tbody><tr><td>Dictionary-based</td><td>0.511</td><td>0.424</td><td>0.641</td></tr><tr><td>Rule-based</td><td>0.472</td><td>0.393</td><td>0.589</td></tr><tr><td>NER</td><td><strong>0.807</strong></td><td><strong>0.743</strong></td><td><strong>0.882</strong></td></tr><tr><td>Text Generation</td><td>0.705</td><td>0.609</td><td>0.838</td></tr></tbody></table>

<table><caption>Table 9: F1-score, recall and precision for different methods for the task of job titles extraction from the DBB dataset. Results for the label ‘position’.</caption><thead><tr><th>Method</th><th>F1 Score</th><th>Recall</th><th>Precision</th></tr></thead><tbody><tr><td>Dictionary-based</td><td>0.562</td><td>0.5</td><td>0.641</td></tr><tr><td>Rule-based</td><td>0.466</td><td>0.416</td><td>0.529</td></tr><tr><td>NER</td><td><strong>0.835</strong></td><td><strong>0.783</strong></td><td><strong>0.893</strong></td></tr><tr><td>Text Generation</td><td>0.766</td><td>0.704</td><td>0.841</td></tr></tbody></table>

<table><caption>Table 10: F1-score, recall and precision for different methods for the task of job titles extraction from the DDB dataset. Results for the label ‘ambiguous’.</caption><thead><tr><th>Method</th><th>F1 Score</th><th>Recall</th><th>Precision</th></tr></thead><tbody><tr><td>Dictionary-based</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Rule-based</td><td><strong>0.098</strong></td><td><strong>0.264</strong></td><td>0.06</td></tr><tr><td>NER</td><td>0.06</td><td>0.145</td><td>0.038</td></tr><tr><td>Text Generation</td><td>0.074</td><td>0.092</td><td><strong>0.121</strong></td></tr></tbody></table>