Abstract: Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.

Introduction: Named Entity Recognition (NER) is an important step in most of the natural language processing (NLP) pipelines. It is designed to robustly handle proper names, which is essential for many applications. Although a seemingly simple task, it faces a number of challenges in noisy datasets and it is still considered an emerging research area BIBREF0 , BIBREF1 . Despite recent efforts, we still face limitations at identifying entities and (consequently) correctly classifying them. Current state-of-the-art NER systems typically have about 85-90% accuracy on news text - such as articles (CoNLL03 shared task data set) - but they still perform poorly (about 30-50% accuracy) on short texts, which do not have implicit linguistic formalism (e.g. punctuation, spelling, spacing, formatting, unorthodox capitalisation, emoticons, abbreviations and hashtags) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF1 . Furthermore, the lack of external knowledge resources is an important gap in the process regardless of writing style BIBREF5 . To face these problems, research has been focusing on microblog-specific information extraction techniques BIBREF2 , BIBREF6 . In this paper, we propose a joint clustering architecture that aims at minimizing the current gap between world knowledge and knowledge available in open domain knowledge bases (e.g., Freebase) for NER systems, by extracting features from unstructured data sources. To this aim, we use images and text from the web as input data. Thus, instead of relying on encoded information and manually annotated resources (the major limitation in NER architectures) we focus on a multi-level approach for discovering named entities, combining text and image features with a final classifier based on a decision tree model. We follow an intuitive and simple idea: some types of images are more related to people (e.g. faces) whereas some others are more related to organisations (e.g. logos), for instance. This principle is applied similarly to the text retrieved from websites: keywords for search engines representing names and surnames of people will often return similarly related texts, for instance. Thus, we derive some indicators (detailed in sec:finalclassifier which are then used as input features in a final classifier. To the best of our knowledge, this is the first report of a NER architecture which aims to provide a priori information based on clusters of images and text features.

Question: What features are extracted from text?
Answer: word feature