There has been little prior work on Named Entity Recognition for ”informal” documents like email. We present two methods for improving performance of person name recognizers for email: emailspecific structural features and a recallenhancing method which exploits name repetition across multiple documents.

There has been little prior work on Named Entity Recognition for ”informal” documents like email. We present two methods for improving performance of person name recognizers for email: emailspecific structural features and a recallenhancing method which exploits name repetition across multiple documents.

Query classification is a subtask of 1CLICK task for selecting appropriate strategy to generate output text. In this paper, we propose to use named entity recognition tools and clue keywords (occupation name list and location type name list) to identify query types.

In some domains, named entity recognition might be considered a solved problem. This does not hold for biological text mining, where protein and gene name recognition are still open research problems [4, 6]. In this paper, we compare two current approaches to the problem of protein name recognition, KeX [5] and Yapex [4]. Unlike manual evaluation which relies on domain experts’ judgement concer...

Named Entity Recognition (NER) from social media posts is a challenging task. User generated content that forms the nature of media, noisy and contains grammatical linguistic errors. This makes it much harder for tasks such as named entity recognition. We propose two novel deep learning approaches utilizing multimodal Transformers. Both our use image features short to provide better results on ...

The Named Entity Recognition (NER) is a task in Information Extraction (IE). The Named entity recognition has become very important for natural language processing. The named entity recognition is defined as the detection and classification of entities from un-structured text where for the Arabic language, the named entity recognition is new in the natural language processing although it has pr...

Most existing methods for biomedical entity recognition task rely on explicit feature engineering where many features either are specific to a particular task or depends on output of other existing NLP tools. Neural architectures have been shown across various domains that efforts for explicit feature design can be reduced. In this work we propose an unified framework using bi-directional long ...

We have explored and implemented different approaches to named entity recognition in German, a difficult task in this language since both regular nouns and proper names are capitalized. Our goal is to identify and recognise person names, geographical names and company names in a computer magazine corpus. Our geographical name classifier works with precompiled lists but our company name classifi...

In this paper we describe the production and consumption of linked data in the scenario of the Italian news agency ANSA portal. The goal of the use-case is to provide viewers of a news item with background information and links to related news articles contained on the portal. This information enrichment process is entity-based: ANSA news archive is analyzed using Name Entity Recognition, and e...

To investigate the problem of entity recognition, we deal with the creation of the so-called Entity Name System (ENS) which is an open, public back-bone infrastructure for the (Semantic) Web that enables the creation and systematic re-use of unique identifiers for entities. The ENS can be seen as a very large, distributed “phonebook for everything”, and ENS identifiers might be considered as a ...