Building Language Models for Text with Named Entities

The goal of the Entity Indexing R&D program at LEXIS-NEXIS is to add controlled vocabulary indexing for named entities to searchable fields in appropriate news

Since most types of factoid questions are answered by named entities, we assume that documents addressing the correct topic but not containing any named entity of the expected

This paper proposes an unsupervised learning model that uses a small-scale named entity dictionary and an unlabeled corpus for classifiying named entities.. Collins and Singer

Instead of annotating a massive amount of data, we leverage a small number of named entities and the large unlabeled data to dis- cover new named entities that can not be

We cast the joint mapping into the following graph problem: mentions from the input text and candidate entities define the node set, and we consider weighted edges between mentions

In the first stage, an input text and available dictionaries are prepared for a dictionary-based named entity detector.. The detector finds can- didates for named entities and

The fact learning module comprises two challenges: Learning feature weights for extracting named entities from text (Named Entity Recognition (NER)) and learning feature weights

We presented NERSO, a named entity recognition and disambiguation system using graph-based scoring method for annotating named entities in a given text using