# On the Use of Parsing for Named Entity Recognition
## 6 Related Work  
There have been a number of articles reviewing the state of the art in NER in a given moment, but none of them had the use of information derived from parsing processes as their main focus, as in this case. The work of [PERSON] and [PERSON] [144], for example, is a classical reference that reviews 15 years of research in NER, from 1991 to 2006. They detected that early systems were making use of handcrafted rule-based algorithms, while modern systems most often resorted to machine learning techniques. Handcrafted systems provided good performance at a relatively high system engineering cost. For machine learning systems, a prerequisite was the availability of a large collection of annotated data, a rather rare resource and limited in domain and language coverage. Indeed, most of the work at that point had concentrated on limited domains and textual genres such as news articles and web pages. The application of syntactic information was limited to the use of fixed syntactic constructions for finding candidate named entities and to the use of syntactic relations (e.g., subject-object) to discover more accurate contextual evidence around entities. Regarding the reviews carried out in the last decade, [PERSON] et al. [145] studied the achievements in the recognition of chemical entities mentioned in text, the determination of their chemical structures, and the identification of relationships between chemicals and other entities. It must be taken into account that chemicals may be referenced in documents in a variety of forms: systematic nomenclatures, common names, trade names, database identifiers, or IUPAC International Chemical Identifier strings; with different types of names having different word morphologies. They classified NER approaches into three categories: dictionary-based, morphology-based, and context-based, the latter category being the only one that involves some form of syntactic parsing guided by manual rules,in contrast to current parsing techniques based on treebank data. At that time, hand-made context-free rules had been proposed to describe a kind of \"chemical language.\" Shallow or template-based parsing had also been considered to mine relationships for entities such as proteins and genes, pharmacogenomics entities, or drug and cytochrome proteins. As a result, parsing was limited most of the time to determining certain components of sentences (e.g., subjects), which were then used in a template matching strategy. NER for the chemical domain was reviewed again a few years later by [PERSON] and [PERSON] [146]. They considered a different classification of NER systems in this domain: dictionary-based, rule-based, machine learning-based, and hybrid approaches. Rule-based systems used a set of hand-made rules to extract the names of entities. The handcrafted models consisted of pattern-based and context-based rules, the latter involving, as before, the use of shallow parsing. In the biomedical domain, [PERSON] et al. [147] analyzed machine learning tools for NER in this context, where it is used to detect entities such as gene, protein, drug, and disease names. It is a complex domain, where many entities are descriptive (e.g., \"normal thymic epithelial cells\"), several entity names can share one head noun, one entity name can have several forms of spelling, and ambiguous abbreviations are frequently used, among other phenomena. The authors detected that three approaches were used at that time to deal with this variety in entity forms: rule-based approaches for names with a strongly defined orthographic and morphological structure, dictionary-based approaches for closely defined vocabularies of names (e.g., diseases and species), and machine learning approaches for highly dynamic vocabularies of names exhibiting strong variability (e.g., genes and proteins).