Introduction
Named entity recognition (NER) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 is the process by which we identify text spans which mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language processing (NLP) applications such as relation extraction BIBREF4 , machine translation BIBREF5 , question answering BIBREF6 and knowledge base construction BIBREF7 . Although early NER systems have been successful in producing adequate recognition accuracy, they often require significant human effort in carefully designing rules or features.
In recent years, deep learning methods been employed in NER systems, yielding state-of-the-art performance. However, the number of types detected are still not sufficient for certain domain-specific applications. For relation extraction, identifying fine-grained types has been shown to significantly increase the performance of the extractor BIBREF8 , BIBREF9 since this helps in filtering out candidate relation types which do not follow this type constraint. Furthermore, for question answering fine-grained Named Entity Recognition (FgNER) can provide additional information helping to match questions to its potential answers thus improving performance BIBREF10 . For example, Li and Roth BIBREF11 rank questions based on their expected answer types (i.e. will the answer be food, vehicle or disease).
Typically, FgNER systems use over a hundred labels, arranged in a hierarchical structure. We find that available training data for FgNER typically contain noisy labels, and creating manually annotated training data for FgNER is a time-consuming process. Furthermore, human annotators will have to assign a subset of correct labels from hundreds of possible labels making this a somewhat arduous task. Currently, FgNER systems use distant supervision BIBREF12 to automatically generate training data. Distant supervision is a technique which maps each entity in the corpus to knowledge bases such as Freebase BIBREF13 , DBpedia BIBREF14 , YAGO BIBREF15 and helps with the generation of labeled data. This method will assign the same set of labels to all mentions of a particular entity in the corpus. For example, “Barack Obama” is a person, politician, lawyer, and author. If a knowledge base has these four matching labels, the distant supervision technique will assign all of them to every mention of “Barack Obama”. Therefore, the training data will also fail to distinguish between mentions of “Barack Obama” in all subsequent utterances.
Ling et al. ling2012fine proposed the first system for FgNER, where they used 112 overlapping labels with a linear classifier perceptron for multi-label classification. Yosef et al. spaniol2012hyena used multiple binary SVM classifiers to assign entities to a set of 505 types. Gillick et al. gillick2014context introduced context dependent FgNER and proposed a set of heuristics for pruning labels that might not be relevant given the local context of the entity. Yogatama et al. yogatama2015embedding proposed an embedding based model where user-defined features and labels were embedded into a low dimensional feature space to facilitate information sharing among labels.
Shimaoka et al. shimaoka2016attentive proposed an attentive neural network model which used long short-term memory (LSTMs) to encode the context of the entity, then used an attention mechanism to allow the model to focus on relevant expressions in the entity mention's context. To learn entity representations, we propose a scheme which is potentially more generalizable.
Datasets
We evaluate our model on two publicly available datasets. The statistics for both are shown in Table TABREF3 . The details of these datasets are as follows:
OntoNotes: OntoNotes 5.0 BIBREF16 includes texts from five different text genres: broadcast conversation (200k), broadcast news (200k), magazine (120k), newswire (625k), and web data (300k). This dataset is annotated with 18 categories.
Wiki(gold): The training data consists of Wikipedia sentences and was automatically generated using a distant supervision method, mapping hyperlinks in Wikipedia articles to Freebase, which we do not use in this study. The test data, mainly consisting of sentences from news reports, was manually annotated as described in BIBREF8 . The class hierarchy is shown in Figure FIGREF2 . This dataset is annotated with 7 main categories (bold text in Figure FIGREF2 ), which maps directly to OntoNotes. The miscellaneous category in Figure FIGREF2 does not have direct mappings, so future work may include redefining these categories so the mappings are more meaningful.
Evaluation Metrics
NER involves identifying both entity boundaries and entity types. With “exact-match evaluation”, a named entity is considered correctly recognized only if both the boundaries and type match the ground truth BIBREF8 , BIBREF17 , BIBREF18 . Precision, Recall, and F-1 scores are computed on the number of true positives (TP), false positives (FP), and false negatives (FN). Their formal definitions are as follows:
True Positive (TP): entities that are recognized by NER and match the ground truth.
False Positive (FP): entities that are recognized by NER but do not match the ground truth.
False Negative (FN): entities annotated in the ground which that are not recognized by NER.
Precision measures the ability of a NER system to present only correct entities, and Recall measures the ability of a NER system to recognize all entities in a corpus. DISPLAYFORM0
The F-1 score is the harmonic mean of precision and recall, and the balanced F-1 score is the variant which is most commonly used. This is defined as: DISPLAYFORM0
Since most NER systems involve multiple entity types, it is often required to assess the performance across all entity classes. Two measures are commonly used for this purpose: the macro-averaged F-1 score and the micro-averaged F-1 score. The macro-averaged F-1 score computes the F-1 score independently for each entity type, then takes the average (hence treating all entity types equally). The micro-averaged F-1 score aggregates the contributions of entities from all classes to compute the average (treating all entities equally). We use the micro-averaged F-1 in our study since this accounts for label imbalances in the evaluation data and therefore a more meaningful statistic.
Method
Over the few past years, the emergence of deep neural networks has fundamentally changed the design of entity detection systems. Consequently, recurrent neural networks (RNN) have found popularity in the field since they are able to learn long term dependencies of sequential data. The recent success of neural network based architectures principally comes from its deep structure. Training a deep neural network, however, is a difficult problem due to vanishing or exploding gradients. In order to solve this, LSTMs were proposed. An LSTM is an internal memory cell controlled by forget gate and input gate networks. A forget gate in an LSTM layer which determines how much prior memory should be passed into the next time increment. Similarly, an input gate scales new input to memory cells. Depending on the states of both gates, LSTM is able to capture long-term or short-term dependencies for sequential data. This is an ideal property for many NLP tasks.
NER using ELMo
Recently, Peters et al. BIBREF19 proposed ELMo word representations. ELMo extends a traditional word embedding model with features produced bidirectionally with character convolutions. It has been shown that the utilization of ELMo for different NLP tasks result in improved performance compared to other types of word embedding models such as Word2Vec BIBREF20 , GloVe BIBREF21 , and fastText BIBREF22 .
The architecture of our proposed model is shown in Figure FIGREF12 . The input is a list of tokens and the output are the predicted entity types. The ELMo embeddings are then used with a residual LSTM to learn informative morphological representations from the character sequence of each token. We then pass this to a softmax layer as a tag decoder to predict the entity types.
Hyperparameter settings: The hidden-layer size of each LSTM within the model is set 512. We use a dropout with the probability of 0.2 on the output of the LSTM encoders. The embedding dimension from ELMo is 1024. The optimization method we use is Adam BIBREF23 . We train with a batch size of 32 for 30 epochs. The model was implemented using the TensorFlow framework.
Entity Linking using Wikidata
Entity linking (EL) BIBREF24 , also known as named entity disambiguation or normalization, is the task to determine the identity of entities mentioned in a piece of text with reference to a knowledge base. There are a number of knowledge bases that provide a background repository for entity classification of this type. For this study, we use Wikidata, which can be seen diagrammatically in Figure FIGREF12 . Systems such as DeepType BIBREF25 integrate symbolic information into the reasoning process of a neural network with a type system and show state-of-the-art performances for EL. They do not, however, quote results on Wiki(gold) so a direct comparison is difficult.
While these knowledge bases provide semantically rich and fine-granular classes and relationship types, the task of entity classification often requires associating coarse-grained classes with discovered surface forms of entities. Most existing studies consider NER and entity linking as two separate tasks, whereas we try to combine the two. It has been shown that one can significantly increase the semantic information carried by a NER system when we successfully linking entities from a deep learning method to the related entities from a knowledge base BIBREF26 , BIBREF27 .
Redirection: For the Wikidata linking element, we recognize that the lookup will be constrained by the most common lookup name for each entity. Consider the utterance (referring to the NBA basketball player) from Figure FIGREF12 “Michael Jeffrey Jordan in San Jose” as an example. The lookup for this entity in Wikidata is “Michael Jordan” and consequently will not be picked up if we were to use an exact string match. A simple method to circumvent such a problem is the usage of a redirection list. Such a list is provided on an entity by entity basis in the “Also known as” section in Wikidata. Using this redirection list, when we do not find an exact string match improves the recall of our model by 5-10%. Moreover, with the example of Michael Jordan (person), using our current framework, we will always refer to the retired basketball player (Q41421). We will never, for instance, pick up Michael Jordan (Q27069141) the American football cornerback. Or in fact any other Michael Jordan, famous or otherwise. One possible method to overcome this is to add a disambiguation layer, which seeks to use context from earlier parts of the text. This is, however, work for future improvement and we only consider the most common version of that entity.
Clustering: The Wikidata taxonomy provides thousands of possible instance of, and subclass of types for our entities. Consequently, in order to perform a meaningful validation of our model, we must find a way to cluster these onto the 112 types provided by Wiki(gold). Our clustering is performed as follows:
If the entity type is either person, location, organization we use the NECKAr BIBREF28 tool to narrow down our list of searchable entities.
We then look at either the occupation for person, or instance of for location/organization categories to map to the available subtypes.
If the entity type is not person, location, or organization we search all of Wikidata.
The clustering we perform in part 1 or 2 is from a cosine similarity of the entity description to the list of possible subtypes for that entity. For this we use Word2Vec word embeddings trained on Wikipedia. We set the minimum threshold of the average cosine similarity to be 0.1.
As an example, consider the test sentence: “The device will be available on sale on 20th April 2011 on amazon uk Apple's iPad” from Figure FIGREF18 . First, we tag iPad as product using the context encoder described in Section 2.1. We then search Wikidata and return the most common variant for that entity in this case Q2796 (the most referenced variant is the one with the lowest Q-id). We then calculate a cosine similarity of the description, in this case “line of tablet computers”, with the possible subtypes of product. The possible subtypes, in this case, are engine, airplane, car, ship, spacecraft, train, camera, mobile phone, computer, software, game, instrument, ship, weapon. We return the highest result above 0.1, which in this case is computer (0.54).
Results
The results for each class type are shown in Table TABREF19 , with some specific examples shown in Figure FIGREF18 . For the Wiki(gold) we quote the micro-averaged F-1 scores for the entire top level entity category. The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%. It is worth noting that one could improve Wiki(gold) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset. The results in Table TABREF19 (OntoNotes) only show the main 7 categories in OntoNotes which map to Wiki(gold) for clarity. The other categories (date, time, norp, language, ordinal, cardinal, quantity, percent, money, law) have F-1 scores between 80-90%, with the exception of time (65%)
Conclusion and Future Work
In this paper, we present a deep neural network model for the task of fine-grained named entity classification using ELMo embeddings and Wikidata. The proposed model learns representations for entity mentions based on its context and incorporates the rich structure of Wikidata to augment these labels into finer-grained subtypes. We can see comparisons of our model made on Wiki(gold) in Table TABREF20 . We note that the model performs similarly to existing systems without being trained or tuned on that particular dataset. Future work may include refining the clustering method described in Section 2.2 to extend to types other than person, location, organization, and also to include disambiguation of entity types.

Question: Which other approaches do they compare their model with?
Answer: Akbik et al. (2018), Link et al. (2012)

They compare to Akbik et al. (2018) and Link et al. (2012).

Question: What results do they achieve using their proposed approach?
Answer: F-1 score on the OntoNotes is 88%, and it is 53% on Wiki (gold).

 total F-1 score on the OntoNotes dataset is 88%, total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%

Question: How do they combine a deep learning model with a knowledge base?
Answer: Entities from a deep learning model are linked to the related entities from a knowledge base by a lookup.

ELMo embeddings are then used with a residual LSTM to learn informative morphological representations from the character sequence of each token