Crowdsourcing for web genre annotation

Recently, genre collection and automatic genre identification for the web has attracted much attention. However, currently there is no genre-annotated corpus of web pages where inter-annotator reliability has been established, i.e. the corpora are either not tested for inter-annotator reliability or exhibit low inter-coder agreement. Annotation has also mostly been carried out by a small number of experts, leading to concerns with regard to scalability of these annotation efforts and transferability of the schemes to annotators outside these small expert groups. In this paper, we tackle these problems by using crowd-sourcing for genre annotation, leading to the Leeds Web Genre Corpus—the first web corpus which is, demonstrably reliably annotated for genre and which can be easily and cost-effectively expanded using naive annotators. We also show that the corpus is source and topic diverse

ADIOS LDA: When Grammar Induction Meets Topic Modeling

We explore the interplay between grammar induction and topic modeling approaches to unsupervised text processing. These two methods complement each other since one allows for the identification of local structures centered around certain key terms, while the other generates a document wide context of expressed topics. This approach allows us to access and identify semantic structures that would be otherwise hardly discovered by using only one of the two aforementioned methods. Using our approach, we are able to provide a deeper understanding of the topic structure by examining inferred information structures characteristic of given topics as well as capture differences in word usage that would be hard by using standard disambiguation methods. We perform our exploration on an extensive corpus of blog posts centered around the surveillance discussion, where we focus on the debate around the Snowden affair. We show how our approach can be used for (semi-) automated content classification and the extraction of semantic features from large textual corpora

Adapting Automatic Summarization to New Sources of Information

English-language news articles are no longer necessarily the best source of information. The Web allows information to spread more quickly and travel farther: first-person accounts of breaking news events pop up on social media, and foreign-language news articles are accessible to, if not immediately understandable by, English-speaking users. This thesis focuses on developing automatic summarization techniques for these new sources of information. We focus on summarizing two specific new sources of information: personal narratives, first-person accounts of exciting or unusual events that are readily found in blog entries and other social media posts, and non-English documents, which must first be translated into English, often introducing translation errors that complicate the summarization process. Personal narratives are a very new area of interest in natural language processing research, and they present two key challenges for summarization. First, unlike many news articles, whose lead sentences serve as summaries of the most important ideas in the articles, personal narratives provide no such shortcuts for determining where important information occurs in within them; second, personal narratives are written informally and colloquially, and unlike news articles, they are rarely edited, so they require heavier editing and rewriting during the summarization process. Non-English documents, whether news or narrative, present yet another source of difficulty on top of any challenges inherent to their genre: they must be translated into English, potentially introducing translation errors and disfluencies that must be identified and corrected during summarization. The bulk of this thesis is dedicated to addressing the challenges of summarizing personal narratives found on the Web. We develop a two-stage summarization system for personal narrative that first extracts sentences containing important content and then rewrites those sentences into summary-appropriate forms. Our content extraction system is inspired by contextualist narrative theory, using changes in writing style throughout a narrative to detect sentences containing important information; it outperforms both graph-based and neural network approaches to sentence extraction for this genre. Our paraphrasing system rewrites the extracted sentences into shorter, standalone summary sentences, learning to mimic the paraphrasing choices of human summarizers more closely than can traditional lexicon- or translation-based paraphrasing approaches. We conclude with a chapter dedicated to summarizing non-English documents written in low-resource languages – documents that would otherwise be unreadable for English-speaking users. We develop a cross-lingual summarization system that performs even heavier editing and rewriting than does our personal narrative paraphrasing system; we create and train on large amounts of synthetic errorful translations of foreign-language documents. Our approach produces fluent English summaries from disdisfluent translations of non-English documents, and it generalizes across languages

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

Document Meta-Information as Weak Supervision for Machine Translation

Data-driven machine translation has advanced considerably since the first pioneering work in the 1990s with recent systems claiming human parity on sentence translation for highresource tasks. However, performance degrades for low-resource domains with no available sentence-parallel training data. Machine translation systems also rarely incorporate the document context beyond the sentence level, ignoring knowledge which is essential for some situations. In this thesis, we aim to address the two issues mentioned above by examining ways to incorporate document-level meta-information into data-driven machine translation. Examples of document meta-information include document authorship and categorization information, as well as cross-lingual correspondences between documents, such as hyperlinks or citations between documents. As this meta-information is much more coarse-grained than reference translations, it constitutes a source of weak supervision for machine translation. We present four cumulatively conducted case studies where we devise and evaluate methods to exploit these sources of weak supervision both in low-resource scenarios where no task-appropriate supervision from parallel data exists, and in a full supervision scenario where weak supervision from document meta-information is used to supplement supervision from sentence-level reference translations. All case studies show improved translation quality when incorporating document meta-information

Creating a Live, Public Short Message Service Corpus: The NUS SMS Corpus

Short Message Service (SMS) messages are largely sent directly from one person to another from their mobile phones. They represent a means of personal communication that is an important communicative artifact in our current digital era. As most existing studies have used private access to SMS corpora, comparative studies using the same raw SMS data has not been possible up to now. We describe our efforts to collect a public SMS corpus to address this problem. We use a battery of methodologies to collect the corpus, paying particular attention to privacy issues to address contributors' concerns. Our live project collects new SMS message submissions, checks their quality and adds the valid messages, releasing the resultant corpus as XML and as SQL dumps, along with corpus statistics, every month. We opportunistically collect as much metadata about the messages and their sender as possible, so as to enable different types of analyses. To date, we have collected about 60,000 messages, focusing on English and Mandarin Chinese.Comment: It contains 31 pages, 6 figures, and 10 tables. It has been submitted to Language Resource and Evaluation Journa

Detecting New, Informative Propositions in Social Media

The ever growing quantity of online text produced makes it increasingly challenging to find new important or useful information. This is especially so when topics of potential interest are not known a-priori, such as in “breaking news stories”. This thesis examines techniques for detecting the emergence of new, interesting information in Social Media. It sets the investigation in the context of a hypothetical knowledge discovery and acquisition system, and addresses two objectives. The first objective addressed is the detection of new topics. The second is filtering of non-informative text from Social Media. A rolling time-slicing approach is proposed for discovery, in which daily frequencies of nouns, named entities, and multiword expressions are compared to their expected daily frequencies, as estimated from previous days using a Poisson model. Trending features, those showing a significant surge in use, in Social Media are potentially interesting. Features that have not shown a similar recent surge in News are selected as indicative of new information. It is demonstrated that surges in nouns and news entities can be detected that predict corresponding surges in mainstream news. Co-occurring trending features are used to create clusters of potentially topic-related documents. Those formed from co-occurrences of named entities are shown to be the most topically coherent. Machine learning based filtering models are proposed for finding informative text in Social Media. News/Non-News and Dialogue Act models are explored using the News annotated Redites corpus of Twitter messages. A simple 5-act Dialogue scheme, used to annotate a small sample thereof, is presented. For both News/Non-News and Informative/Non-Informative classification tasks, using non-lexical message features produces more discriminative and robust classification models than using message terms alone. The combination of all investigated features yield the most accurate models