# On the Use of Parsing for Named Entity Recognition
## 6 Related Work
Although they did not consider parsing to be one of the most widely used resources, they cited it as one of the NLP techniques used in NER systems. In the clinical domain, NER essentially focuses on two types of entities: personal health identifiers in the context of clinical document de-identification and clinical entities such as diseases, signs/symptoms, procedures or medications, as well as their context of occurrence: negation, assertions, and experiencer (i.e., whether the entities are relevant to the patient or a third party such as a family member or organ donor). They claimed that negation may be easily adapted between languages of the same family that express negation using similar syntactic structures. [PERSON] and [PERSON] [54] presented a survey of deep neural network architectures for NER and contrasted them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. With respect to the use of parsing and syntax, they only briefly cited that shallow syntactic knowledge can be useful as a feature for unsupervised NER systems. More recently, [PERSON] and [PERSON] [153] reported the latest developments in medical NER for two selected semantic classes, diseases and drugs (or medications), and relations between them. They focused their review on the methodological paradigm shift from standard machine learning techniques to deep learning. They concluded that deep-learning-based approaches outperform classical machine learning ones but, at the same time, small-sized and access-limited corpora create intrinsic problems for data-greedy deep learning. The same applies to special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies. No mention was made of the use of syntactic information beyond indicating that clinical notes and reports often exhibit syntactically ill-formed, telegraphic language. Finally, [PERSON] et al. [53] reviewed in detail existing deep learning techniques for NER, systematically categorizing approaches based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Although several deep artificial neural network models try to represent long-distance dependencies that have a clear syntactic component, they do so in an implicit way, through a sequential process in which relevant information is remembered and propagated so that the representation associated with a given word can include non-local information coming from a different location in the sentence. However, some systems resort to information derived by parsers, such as dependency roles, to build complex distributed representations of words. [PERSON] et al. [154] presented a comprehensive survey of deep learning models for other classification tasks. IOB is the most widely used labeling scheme in NER, but it is not the only one. Recently, [PERSON] et al. [155] proposed several constituent-based (The term _constituent_ in _Constituent-based tagging scheme_ does not refer to constituents in the sense of constituency parsing, but to each of the elements that constitute (are part of) a named entity or time expression) labeling schemes instead of the traditional IOB positional labeling scheme. More specifically, they define a TOMN scheme to model temporal expressions, where T refers to Time token, M to Modifier, N to Numeral and O to Outside time expressions; and an UGTO scheme to model named entities, where U refers to Uncommon word, G to General modifier, T to Trigger word and O to Outside named entities. Experimental results show that CRF-based methods using these constituent-based labeling schemes perform equally to, or more effectively than, representative state-of-the-art methods on time expression extraction and named entity extraction.