Named Entity Recognition (NER) Sample Clauses

Named Entity Recognition (NER). In EUMSSI, the Stanford NER tool5 [Xxxxxx et al 2005] is used. Illinois NER6 has also been experimented with, but since Stanford NER provides very good results and is already integrated in DKPro, it has been chosen over other tools. Stanford NER detects named entities and categorise them into types or classes. E.g. “New York” is a named entity of type location. Stanford NER uses a combination of conditional random field (CFR) sequence taggers trained on various corpora and a few additional features such as gazetteers to recognize named entities. For more details see [Xxxxxxx et al 2014] and [Xxxxxx et al 2005]. The advantages of Stanford NER are:

Named Entity Recognition (NER). ‌ As a baseline for our work we use the state of the art NER framework Stanford CoreNLP[3]. Stanford CoreNLP is a Java annotation pipeline framework, which pro- vides most of the common core natural language processing (NLP) steps [4]. We in- stanfordcorenlp tegrate the Stanford CoreNLP by including the package in our tool. The CoreNLP toolkit provides several functionalities named ”annotators”. In our tool, we mainly use the following two annotators: • The tokenization which is the task of chopping into pieces a textual document into tokens. A token is a sequence of characters which represents a semantic unit. • Named Entity Recognition where the task is to extract so-called named entities from the text. Named entities are the chunks of words from text, which refer to an entity of certain type. CoreNLP is able to recognize named (PERSON, LOCA- TION, ORGANIZATION, MISC) and numerical (MONEY, NUMBER, DATE, TIME, DURATION, SET) entities. We use it mainly to check if a mentioned brand name is recognized as an organization.