Adapting Sequence to Sequence models for Text Normalization in Social Media

Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.Comment: Accepted at the 13th International AAAI Conference on Web and Social Media (ICWSM 2019

Generating multimedia presentations: from plain text to screenplay

In many Natural Language Generation (NLG) applications, the output is limited to plain text – i.e., a string of words with punctuation and paragraph breaks, but no indications for layout, or pictures, or dialogue. In several projects, we have begun to explore NLG applications in which these extra media are brought into play. This paper gives an informal account of what we have learned. For coherence, we focus on the domain of patient information leaflets, and follow an example in which the same content is expressed first in plain text, then in formatted text, then in text with pictures, and finally in a dialogue script that can be performed by two animated agents. We show how the same meaning can be mapped to realisation patterns in different media, and how the expanded options for expressing meaning are related to the perceived style and tone of the presentation. Throughout, we stress that the extra media are not simple added to plain text, but integrated with it: thus the use of formatting, or pictures, or dialogue, may require radical rewording of the text itself

Method or Madness? Textual analysis in media studies

Scholarly analyses of media have tended to view the media text (e.g. film / programme / article) as the logical site of enquiry. However, this focus on the text has often resulted in a privileging of the text as the locus of meaning. The validity of textual analysis as a research method has increasingly been called into question due to the influence of poststructuralist theories and the critique of textually-based research emerging from the ‘new audience studies’. In this paper I examine the debates surrounding texts, audiences and meanings from a poststructuralist perspective. I argue that the rethinking of subjectivity achieved by discourse theory provides the key to a new conception of textual analysis, which remains a vital and rewarding approach to the study of media and culture

Alien nation: contemporary art and black Britain

About the book: This fascinating text introduces readers to postcolonial theory using the context of British media culture in ethnic minority communities to explain key ideas and debates. Each chapter considers a specific media output and uses a wealth of examples to offer an absorbing insight into postcolonial media for all students of cultural and media studies