# On the Use of Parsing for Named Entity Recognition
###### Abstract  
Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task.  
natural language processing; named entity recognition; parsing; sequence labeling +
Footnote †: journal: _Sci._  
0021202111090  
the exact span of entities (e.g., in \"post-COVID-19 pneumonia pulmonary fibrosis,\" the constituent boundaries of \"pulmonary fibrosis\" delimit an entity distinct from \"pneumonia\").  
_Syntactic parsing_, the task of determining the structure of a sentence, is a key task in many NLP applications that need to process text or utterances beyond a shallow level, extracting meaning or relations between objects, entities, or events referred to in the text. Syntactic parsing takes two main forms, depending on the intended output: in _dependency parsing_, the syntactic structure is expressed by means of binary directed relations between words, called dependencies; while in _constituency parsing_ (or phrase structure parsing), it is represented as a phrase structure tree that divides the sentence recursively into its constituent units. _Semantic parsing_ goes a step further by converting natural language sentences to logical forms following various representation languages, such as Abstract Meaning Representations (AMRs) [11].  
In this article, we focus on the use of the information resulting from the parsing process in the NER task. Toward this aim, we start in Section 2 by defining NER, discussing the use of sequence labeling in NLP, and framing NER as a sequence labeling task. We also review the main resources applied to the task and the evaluation measures commonly used. In Section 3 we define parsing and analyze the latest developments in this area that make parsing a convenient tool to be used in NER. We continue in Section 4 with a study of the most relevant NER systems that use parsing information, including both those that use it as a source of features in a sequence labeling setting and those that make use of parsing results to guide the NER process. In Section 5 we discuss the results achieved, summarize the different approaches in tabular form, present a new proposal for the use of parsing in NER based on the consideration of the parsing process itself as a sequential labeling process, and we compare it with a sequence-to-sequence approach. In Section 6 we analyze work related to this article, to finally elaborate the conclusions in Section 7.