# On the Use of Parsing for Named Entity Recognition
## 6 Related Work
They focused their survey on the latter and they detected that shallow syntactic parsing benefits pre-processing of gene and protein names, particularly when using _chunking_ to divide the text into syntactically correlated parts of words (e.g., noun or verb phrases). They also observed that, given that these linguistic units only provide a local analysis of some tokens in the sentence, additional information can be derived from dependency parsing to collect the relations between a wider range of tokens. NER for the biomedical domain was reviewed again several years later by [PERSON] and [PERSON] [148]. At that time, most methods were relying on machine learning techniques and they reviewed some features that could be used in such techniques, such as morphological features, dictionary-based features, lexical features and distance-based features, but not syntactic features. A different perspective was taken by [PERSON] et al. [149], who analyzed the evolution of NER from a theoretical and practical point of view, arguing that the task was actually far from being solved and showing the consequences for the development and evaluation of tools. They focused their review around what the task of NER is in itself, analyzing the different meanings of the term _named entity_. They also analyzed the resources and metrics that were used to solve the task and to measure the results attained, concluding that systems were overfitting to the training corpora, leading to serious limitations in the external validity of NER evaluations, given that systems did not perform well in general but for a particular user and document type. Another context-specific review work is that of [PERSON] [150], who studied the features of common tools used for NER in the Arabic language. This language poses particular challenges for NER, such as the use of the Arabic script; the co-existence of Classic Arabic, Modern Standard Arabic, and Colloquial Arabic dialects; lack of capitalization; lack of uniformity in writing styles; optional short vowels; and agglutination. As a sample of the complexity of the task in this language, we have that two mentions to entities may appear in one word, given that a pronominal can appear as a suffix pronoun to a nominal. One of the primary approaches for Arabic NER was based on handcrafted local grammatical rules. The structure of Arabic sentences allows a named entity to appear anywhere in the sentence and at different distances from lexical triggers, which complicated the structure of the rules. This led to using base-phrase chunks such as noun phrases and verb phrases, identified by means of shallow syntactic parsing. The other primary approach was based on machine learning classifiers, where syntactic information could also be used, giving rise to hybrid approaches. [PERSON] et al. [151] presented the status of NER techniques developed by the research community and identified the issues and challenges (nested entities, ambiguity, annotation of training data, lack of resources) as well as factors (language, text genre, text domain) affecting NER performance, all of them to be considered when designing these systems. They found that earlier systems were most often based on handcrafted rules, including rules based on syntactic-lexical patterns to identify and classify named entities. These systems are highly efficient because they exploit the properties of language-related knowledge, employing domain-specific features to obtain sufficient accuracy. However, they are quite expensive, domain-specific and non-portable. In the case of NER systems based on machine learning, some of them consider chunks of text detected by means of shallow parsing as features. [PERSON] et al. [152] offered an overview of clinical NLP for non-English languages. In the case of NER, they found that, similar to approaches for English, the methods for other languages are rule-based, statistical, or a combination of both.