In this paper, we present a robust incremental architecture for natural language processing centered around syntactic analysis but allowing at the same time the description of specialized modules, like named entity recognition. We show that the flexibility of our approach allows us to intertwine general and specific processing, which has a mutual improvement effect on their respective results: ...

Named Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in the biomedical domain, enabling knowledge discovery from medical texts. Due to the fact that for the Romanian language there are only a few linguistic resources specific to the biomedical domain, we have created a sub-corpus specific to this domain. In this paper we present a new...

Due to the rapidly increasing amount of biomedical literature, automatic processing of biomedical papers is extremely important. Named Entity Recognition (NER) in this type of writing has several difficulties. In this paper we present a system to find phenotype names in biomedical literature. The system is based on Metamap and makes use of the UMLS Metathesaurus and the Human Phenotype Ontology...

In this paper, we describe our approach for Named Entity rEcognition and Linking Challenge (NEEL) at the #Microposts2016. The task is to automatically recognize entities and their types from English microposts, and link them to corresponding DBpedia 2015 entries. If the resources do not exist, we use NIL identifiers instead. The task is unique as twitter data is informal in nature with non-conf...

Named Entity Recognition (NER) for Amazigh language is a potentially useful pretreatment for many processing applications for the Amazigh language. However, this task represents a tough challenge, given the specificities of this language. In this paper, we present (NERAM) the first named entity system for the Amazigh language based on a symbolic approach that uses linguistic rules built manuall...

In this demo paper, we present NEED4Tweet, a Twitterbot for named entity extraction (NEE) and disambiguation (NED) for Tweets. The straightforward application of state-of-the-art extraction and disambiguation approaches on informal text widely used in Tweets, typically results in significantly degraded performance due to the lack of formal structure; the lack of sufficient context required; and...

In this paper, we describe our approach for Named Entity Recognition in Twitter, a shared task for ACL 2015 Workshop on Noisy User-generated Text (Baldwin et al., 2015). Because of the noisy, short, and colloquial nature of Twitter, the performance of Named Entity Recognition (NER) degrades significantly. To address this problem, we propose a novel method to enhance the performance of the Twitt...

This paper describes our approaches for the preparation of gazetteers for named entity recognition (NER) in Indian languages. We have described two methodologies for the preparation of gazetteers1. Since the relevant gazetteer lists are more easily available in English we have used a transliteration based approach to convert available English name lists to Indian languages. The second approach ...

We consider a novel setting for Named Entity Recognition (NER) where we have access to document-specific knowledge base tags. These tags consist of a canonical name from a knowledge base (KB) and entity type, but are not aligned to the text. We explore how to use KB tags to create document-specific gazetteers at inference time to improve NER. We find that this kind of supervision helps recognis...

This paper presents a multilingual system designed to recognize named entities in a wide variety of languages (currently more than 12 languages are concerned). The system includes original strategies to deal with a wide variety of encoding character sets, analysis strategies and algorithms to process these languages.