# A New Approach to Information Extraction in User-Centric E-Recruitment Systems
## 2 Related Work
### Information Extraction  
_Information extraction_ is a technique for identification of entities from unstructured text [10]. A job description contains entities and compound words, such as job titles, job requirements, job responsibilities, career level and others entities that need to be extracted. Extraction techniques, to cater for unstructured document formats, mainly focus on rules and pattern [11], machine learning [12] and ontology [13] based approaches. _Rule and pattern-based techniques_ identify hidden features in the text by utilizing predefined rules or known patterns [11], e.g., extraction of a person's phone number may require the occurrence of phrases, such as \"at\", \"can be (reached | called) at\" and \"s number is\". Absence of these phrases will result in the non-extraction of the person's phone number. Rule-based techniques have been applied in multiple domains, such as aspect extraction of product reviews by exploiting common-sense knowledge and sentence dependency trees [14], relation extraction using background knowledge [15], extraction of patient's clinical data from medical texts [16] and numerous others. These methods may process unstructured text multiple times to get meaningful information. Besides this, rule based techniques have been applied in the extraction of compound entities from bio-medial domains [17] using BioInfer and GENIA corpuses. The drawback of compound word extraction using the technique mentioned by [PERSON] et al. [17] is that any concept that is missing in the BioInfer and GENIA corpuses will not be identified as a compound word. These methods cannot identify any new rule or pattern that is not already defined. Machine learning based techniques can deal with such problems. _Machine learning based techniques_ help in the extraction of existing and new information from unstructured texts. Machine learning techniques use Hidden Markov Models (HMM) [18] and Conditional Random Fields (CRF) [19] to extract information from unstructured text. Machine learning based techniques require large data-sets for training and evaluation purposes. The aforementioned techniques fail to link information together with context. The lack of context may result in information loss. _Ontology based techniques_ cover this gap in information extraction, and mainly use domain-specific knowledge for extracting meaningful information from unstructured text [20]. Some well known existing systems that use domain ontology are KIM [21] and TextPresso [22]. These systems only use information present in domain ontology to facilitate entity extraction. TextPresso mainly focuses on entity extraction in the bio-medical domain. It uses Gene Ontology (GO) during extraction that comprises approximately 80% of the lexicon. Any new information extracted will result in information loss. This limitation was addressed by the technique proposed by [PERSON] et al. [13]. According to their technique, the newly extracted knowledge is merged with existing domain knowledge resulting in enhanced domain knowledge. Further down the road, information extraction has also been supported with fuzzy ontology [23] and used in extracting travelers' reviews about hotels, building and designing business intelligence systems for gathering company intelligence and country/region information [24], building and designing systems to extract information from clinical documents such as admission reports, radiology findings and discharge letters [25], and a framework for retrieval of images from web data [26]. The technique proposed by [PERSON] et al. [13] has the limitation of only using the lexical English database WordNet ([[https://wordnet.princeton.edu/](https://wordnet.princeton.edu/)]([https://wordnet.princeton.edu/](https://wordnet.princeton.edu/))) as an external source for enhancing information. The enhancement was limited to WordNet which suffers a staleness issue from data.