The problem of entity-typing has been studied predominantly in supervised learning fashion, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets, they often lack the flexibility to transfer across text genres and to generalize to new type taxonomies. In this work we propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Given a type taxonomy defined as Boolean functions of FREEBASE "types", we ground a given mention to a set of type-compatible Wikipedia entries and then infer the target mention's types using an inference algorithm that makes use of the types of these entries. We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain. Our system is shown to be competitive with state-of-the-art supervised NER systems and outperforms them on out-of-domain datasets. We also show that our system significantly outperforms other zero-shot fine typing systems.
Named Entity Recognition (NER), for which the goal is to discover mention-boundaries in addition to typing, often using a small set of mutually exclusive types, has a considerable amount of work @cite_37 @cite_21 @cite_0 @cite_10 @cite_7 .
@cite_37 @cite_7 @cite_21 @cite_0 @cite_10
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Named entity recognition (ner) for English typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. Recent work has used Wikipedia to automatically create a massive corpus of named entity annotated text. We present the first comprehensive cross-corpus evaluation of ner. We identify the causes of poor cross-corpus performance and demonstrate ways of making them more compatible. Using our process, we develop a Wikipedia corpus which outperforms gold standard corpora on cross-corpus evaluation by up to 11 . Named Entity Recognition (NER) is the task of locating and classifying names in text. In previous work, NER was limited to a small number of pre-defined entity classes (e.g., people, locations, and organizations). However, NER on the Web is a far more challenging problem. Complex names (e.g., film or book titles) can be very difficult to pick out precisely from text. Further, the Web contains a wide variety of entity classes, which are not known in advance. Thus, hand-tagging examples of each entity class is impractical. This paper investigates a novel approach to the first step in Web NER: locating complex named entities in Web text. Our key observation is that named entities can be viewed as a species of multiword units, which can be detected by accumulating n-gram statistics over the Web corpus. We show that this statistical method's F1 score is 50 higher than that of supervised techniques including Conditional Random Fields (CRFs) and Conditional Markov Models (CMMs) when applied to complex names. The method also outperforms CMMs and CRFs by 117 on entity classes absent from the training data. Finally, our method outperforms a semi-supervised CRF by 73 . Named Entity Recognition (NER) is a subtask of information extraction and aims to identify atomic entities in text that fall into predefined categories such as person, location, organization, etc. Recent efforts in NER try to extract entities and link them to linked data entities. Linked data is a term used for data resources that are created using semantic web standards such as DBpedia. There are a number of online tools that try to identify named entities in text and link them to linked data resources. Although one can use these tools via their APIs and web interfaces, they use different data resources and different techniques to identify named entities and not all of them reveal this information. One of the major tasks in NER is disambiguation that is identifying the right entity among a number of entities with the same names; for example "apple" standing for both "Apple, Inc." the company and the fruit. We developed a similar tool called NERSO, short for Named Entity Recognition Using Semantic Open Data, to automatically extract named entities, disambiguating and linking them to DBpedia entities. Our disambiguation method is based on constructing a graph of linked data entities and scoring them using a graph-based centrality algorithm. We evaluate our system by comparing its performance with two publicly available NER tools. The results show that NERSO performs better. This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5 and 11.3 F-1 score improvement over co-training on disease and virus NER respectively. We also show that by adding candidate phrase embeddings as features in a sequence tagger gives better performance compared to using word embeddings.