# On the Use of Parsing for Named Entity Recognition
## 4 Parsing for NER
### Using Complete Parse Trees for NER  
In the case of [PERSON] et al. [135], they proposed to use a statistical parsing technique to simultaneously identify biomedical named entities and extract subcellular localization relations for bacterial proteins. In their approach, sentences are automatically annotated by a statistical parser. Then, the constituency parse trees are decorated with annotations on relevant protein, bacterium, and location named entities; and annotations on the path linking related entities in the parse tree of each sentence. Experiments with purely supervised learning showed that in order to be effective, the model required a large curated set to minimize the sparse data problem. Unfortunately, domain-specific annotated corpora are rare and expensive, so the authors decided to add noisy texts (i.e., with automatically labeled named entities and relations) to the training set. By doing this, the system reaches a competitive performance. [PERSON] and [PERSON] [136] proposed a joint model of context-free parsing and named entity recognition, based on a discriminative CRF-based constituency parser. They found that combining parsing and NER improves performance on both tasks, the joint model producing an output that has consistent parse structure and named entity spans and, moreover, is also doing a better job at both tasks than separate models with the same features. This joint model proceeds as follows. In a first phase, the set of constituency trees from the OntoNotes 2.0 data set is modified. As named entities correspond to phrasal nodes, the labels of such phrasal nodes and their descendants in each parse tree are augmented with the type of those named entities. Some additional manual modifications are required, such as removing final periods from the entity annotation, flattening nested noun phrases, and moving adjectives from nested noun phrases into the main noun phrase. The augmented nodes give place to extra copies of the source grammar where named entities are taken into account. This representation will even be able to handle nested named entities in a natural way, although the data set used for evaluation does not contain such entities. This grammar is used by a CRF-based parser that considers features over both the parse rules and the named entities. In the testing phase, the parser analyzes each sentence, and the named entities are extracted from the parse tree. As we can see, this approach differs from the others described in this article in that NER is not considered as a sequence labeling task but rather as a by-product of a parsing process. One of the practical difficulties of this approach is that the size of the corpus employed is much smaller than the treebanks on which parsers are routinely trained, at least for English. Instead of constituency parsers, [PERSON] et al. [137] proposed a NER model guided by a dependency parser. The basis of this approach is that named entities tend to be covered by single or multiple adjacent dependency arcs, since certain internal structures are expected to exist for most named entities that convey semantically meaningful information. As a result, words inside each named entity typically do not have dependencies with words outside the entities, except for certain words such as head words, which often have incoming arcs from outside words. Thus, the authors derive a semi-Markov CRF model by restricting the space of all possible combinations of entities to those that strictly contain only valid spans, where a valid span either consists of a single word, or is a word sequence that is covered by a chain of dependency arcs where no arc is covered by another. This model performs competitively with respect to conventional linear CRF-based models and exhibits the same time complexity. Finally, [PERSON] et al. [95] introduced a method to handle both flat and nested named entities by adopting ideas from the biaffine dependency parsing model [107].