People tweet more than 100 Million times daily, yielding a noisy, informal, but sometimes
informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented …

Matrix factorization approaches to relation extraction provide several attractive features: they
support distant supervision, handle open schemas, and leverage unlabeled data …

Abstract The challenges of Named Entities Recognition (NER) for tweets lie in the
insufficient information in a tweet and the unavailability of training data. We propose to …

Abstract The task of Named Entity Recognition (NER) is aimed at identifying named entities
in a given text and classifying them into pre-defined domain entity types such as persons …

Named-entity recognition (NER) is an important task required in a wide variety of
applications. While rule-based systems are appealing due to their well-known …

In this paper we consider the problem of labeling the languages of words in mixed-language
documents. This problem is approached in a weakly supervised fashion, as a sequence …

Tweets represent a critical source of fresh information, in which named entities occur
frequently with rich variations. We study the problem of named entity normalization (NEN) for …

Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient
information in a tweet and the lack of training data. We propose a novel method consisting of …

As the Internet and World Wide Web have continued to gain widespread adoption, the
linguistic diversity represented has also been growing. Simultaneously the field of …

One main challenge of Named Entities Recognition (NER) for tweets is the insufficient
information in a single tweet, owing to the noisy and short nature of tweets. We propose a …