# On the Use of Parsing for Named Entity Recognition
## 4 Parsing for NER
### Syntactic Information as a Feature for Sequence Labeling NER  
[PERSON] and [PERSON] (2016) presented a system for Japanese NER based on an SVM classifier that uses several types of structural information, such as that obtained from the head verb of a sentence by means of a syntactic parser and the surface case of the phrase that includes a target entity. To deal with head verbs that do not appear in the training data, case frames are introduced. Case frames describe what kinds of cases each verb can have and what kinds of nouns can fill a case slot. They are learned from a corpus of five hundred million sentences: firstly, entities are detected by a primitive NER system that uses only local features; secondly, case frames are constructed from the sentences containing such entities. Thus, if a given threshold percentage of the examples of a case are classified as pertaining to a certain entity class, the corresponding label is attached to the case. By using all structural information, the performance improves significantly for all data sets, which means that structural information improves the performance of Japanese NER. In particular, syntactic features improve the performance not dramatically, but consistently and independently from the data set. This result also shows that case frame features are general features that can be effective for data from different domains. In their work (2017), [PERSON] and [PERSON] described FIGER, a fine-grained entity recognizer that identifies references to entities in natural language text and labels them with appropriate tags from a set of 112 tags. The training set for these tags is created by exploiting the anchor links in Wikipedia text to automatically label entity segments with suitable tags. A CRF model is trained for segmentation, identifying the boundaries of each text segment that mentions an entity. An adapted perceptron algorithm is used as the final classifier in charge of assigning tags to the detected entities, considering both word-based features (including unigram and bigram features) and syntax-based features such as the head of the segment containing the entity and the syntactic dependency of said head. Compared to standard NER systems, FIGER shows a higher performance. An error analysis detects that most errors originate from noise in the training data. It is worth mentioning that training data were created without resorting to parsing. The proposed approach of [PERSON] et al. (2018) to chemical NER is based on a neural network. The base classifier is an attention-based bidirectional LSTM with a conditional random field layer, thus trying to leverage document-level global information obtained by an attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. This approach achieves better performance on chemical compound and drug name recognition than other state-of-the-art methods, while requiring little feature engineering. In particular, the authors investigated the effect of linguistic features such as PoS tags and chunks obtained through shallow parsing. The baselines take word and character embeddings as inputs to the model while the additional features are introduced into the deep learning classifier as additional embeddings. Without the attention mechanism, the highest F-score is achieved when only the chunking embedding is added, the main reason being that some entity boundary errors can be revised by thechunking information. When only the PoS embedding is added, the model achieves a smaller improvement. However, with the attention mechanism in place, the contribution of these features to the performance of the model is negligible. Although these studies demonstrate the potential benefits of incorporating syntactic information, they are limited in either treating noisy syntactic information as gold references for training their taggers, or using direct concatenation to combine that information with context information without weighing it with respect to its contribution to the NER task. [PERSON] et al.