Mining Twitter for crisis management: realtime floods detection in the Arabian Peninsula

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of doctor of Philosophy.In recent years, large amounts of data have been made available on microblog platforms such as Twitter, however, it is difficult to filter and extract information and knowledge from such data because of the high volume, including noisy data. On Twitter, the general public are able to report real-world events such as floods in real time, and act as social sensors. Consequently, it is beneficial to have a method that can detect flood events automatically in real time to help governmental authorities, such as crisis management authorities, to detect the event and make decisions during the early stages of the event. This thesis proposes a real time flood detection system by mining Arabic Tweets using machine learning and data mining techniques. The proposed system comprises five main components: data collection, pre-processing, flooding event extract, location inferring, location named entity link, and flooding event visualisation. An effective method of flood detection from Arabic tweets is presented and evaluated by using supervised learning techniques. Furthermore, this work presents a location named entity inferring method based on the Learning to Search method, the results show that the proposed method outperformed the existing systems with significantly higher accuracy in tasks of inferring flood locations from tweets which are written in colloquial Arabic. For the location named entity link, a method has been designed by utilising Google API services as a knowledge base to extract accurate geocode coordinates that are associated with location named entities mentioned in tweets. The results show that the proposed location link method locate 56.8% of tweets with a distance range of 0 – 10 km from the actual location. Further analysis has shown that the accuracy in locating tweets in an actual city and region are 78.9% and 84.2% respectively

Conversational Entity Linking: Problem Definition and Datasets

Machine understanding of user utterances in conversational systems is of utmost importance for enabling engaging and meaningful conversations with users. Entity Linking (EL) is one of the means of text understanding, with proven efficacy for various downstream tasks in information retrieval. In this paper, we study entity linking for conversational systems. To develop a better understanding of what EL in a conversational setting entails, we analyze a large number of dialogues from existing conversational datasets and annotate references to concepts, named entities, and personal entities using crowdsourcing. Based on the annotated dialogues, we identify the main characteristics of conversational entity linking. Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting. The resources released with this paper include annotated datasets, detailed descriptions of crowdsourcing setups, as well as the annotations produced by various EL systems. These new resources allow for an investigation of how the role of entities in conversations is different from that in documents or isolated short text utterances like queries and tweets, and complement existing conversational datasets.publishedVersio

Generalisation in named entity recognition: A quantitative analysis

Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall. In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data. Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire. Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F1 scores for diverse genres as compared to more regular ones. We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation

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

How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?

A vast amount of geographic information exists in natural language texts, such as tweets and news. Extracting geographic information from texts is called Geoparsing, which includes two subtasks: toponym recognition and toponym disambiguation, i.e., to identify the geospatial representations of toponyms. This paper focuses on toponym disambiguation, which is usually approached by toponym resolution and entity linking. Recently, many novel approaches have been proposed, especially deep learning-based approaches, such as CamCoder, GENRE, and BLINK. In this paper, a spatial clustering-based voting approach that combines several individual approaches is proposed to improve SOTA performance in terms of robustness and generalizability. Experiments are conducted to compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly ambiguous and challenging datasets (e.g., WikToR and CLDW). The datasets are of six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing in total 98,300 places across the world. The results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving the generalizability and robustness of the voting approach. Also, the voting ensemble drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways.Comment: 32 pages, 15 figure

How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?

A vast amount of geospatial information exists in natural language texts, such as tweets and news. Extracting geospatial information from texts is called Geoparsing, which includes two subtasks: toponym recognition and toponym disambiguation, i.e., to identify the geospatial representations of toponyms. This paper focuses on toponym disambiguation, which is approached by toponym resolution and entity linking. Recently, many novel approaches have been proposed, especially deep learning-based, such as CamCoder, GENRE, and BLINK. In this paper, a spatial clustering-based voting approach combining several individual approaches is proposed to improve SOTA performance regarding robustness and generalizability. Experiments are conducted to compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly challenging datasets (e.g., WikToR). They are in six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing 98,300 places across the world. Experimental results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving its generalizability and robustness. Besides, it drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways