Collective Approaches to Named Entity Disambiguation

Internet content has become one of the most important resources of information. Much of this information is in the form of natural language text and one of the important components of natural language text is named entities. So automatic recognition and classification of named entities has attracted researchers for many years. Named entities are mentioned in different textual forms in different documents. Also, the same textual mention may refer to different named entities. This problem is well known in NLP as a disambiguation problem. Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretations in a specific knowledge base (KB). NED is important for many applications like search engines and software agents that aim to aggregate information on real world entities from sources such as the Web. The main goal of this research is to develop new methods for named entity disambiguation, emphasising the importance of interdependency of named entity candidates of different textual mentions in the document. The thesis focuses on two connected problems related to disambiguation. The first is Candidates Generation, the process of finding a small set of named entity candidate entries in the knowledge base for a specific textual mention, where this set contains the correct entry in the knowledge base. The second problem is Collective Disambiguation, where all named entity textual mentions in the document are disambiguated jointly, using interdependence and semantic relations between the different NE candidates of different textual mentions. Wikipedia is used as a reference knowledge base in this research. An information retrieval framework is used to generate the named entity candidates for a textual mention. A novel document similarity function (NEBSim) based on NE co-occurrence is introduced to calculate the similarity between two documents given a specific named entity textual mention. NEB-sim is also used in conjunction with the traditional cosine similarity measure to learn a model for ranking the named entity candidates. Na\"{i}ve Bayes and SVM classifiers are used to re-rank the retrieved documents. Our experiments, carried out on TAC-KBP 2011 data, show NEBsim achieves significant improvement in accuracy as compared with a cosine similarity approach. Two novel approaches to collectively disambiguate textual mentions of named entities against Wikipedia are developed and tested using the AIDA dataset. The first represents the conditional dependencies between different named entities across Wikipedia as a Markov network, where named entities are treated as hidden variables and textual mentions as observations. The number of states and observations is huge, and na\"{i}vely using the Viterbi algorithm to find the hidden state sequence which emits the query observation sequence is computationally infeasible given a state space of this size. Based on an observation that is specific to the disambiguation problem, we develop an approach that uses a tailored approximation to reduce the size of the state space, making the Viterbi algorithm feasible. Results show good improvement in disambiguation accuracy relative to the baseline approach, and to some state-of-the-art approaches. Our approach also shows how, with suitable approximations, HMMs can be used in such large-scale state space problems. The second collective disambiguation approach uses a graph model, where all possible NE candidates are represented as nodes in the graph, and associations between different candidates are represented by edges between the nodes. Each node has an initial confidence score, e.g. entity popularity. Page-Rank is used to rank nodes, and the final rank is combined with the initial confidence for candidate selection. Experiments show the effectiveness of using Page-Rank in conjunction with initial confidence, achieving 87\% accuracy, outperforming both baseline and state-of-the-art approaches

USFD at KBP 2011: Entity Linking, Slot Filling and Temporal Bounding

This paper describes the University of Sheffield's entry in the 2011 TAC KBP entity linking and slot filling tasks. We chose to participate in the monolingual entity linking task, the monolingual slot filling task and the temporal slot filling tasks. We set out to build a framework for experimentation with knowledge base population. This framework was created, and applied to multiple KBP tasks. We demonstrated that our proposed framework is effective and suitable for collaborative development efforts, as well as useful in a teaching environment. Finally we present results that, while very modest, provide improvements an order of magnitude greater than our 2010 attempt.Comment: Proc. Text Analysis Conference (2011

The QMUL/HRBDT contribution to the NADI Arabic Dialect Identification Shared Task

We present the Arabic dialect identification system that we used for the country-level subtask of the NADI challenge. Our model consists of three components: BiLSTM-CNN, character-level TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieved an F1-Macro score of 20.77% and an accuracy of 34.32% on the test set. The model was also evaluated on the Arabic Online Commentary dataset, achieving results better than the state-of-the-art

An NLP-Powered Human Rights Monitoring Platform

Effective information management has long been a problem in organisations that are not of a scale that they can afford their own department dedicated to this task. Growing information overload has made this problem even more pronounced. On the other hand we have recently witnessed the emergence of intelligent tools, packages and resources that made it possible to rapidly transfer knowledge from the academic community to industry, government and other potential beneficiaries. Here we demonstrate how adopting state-of-the-art natural language processing (NLP) and crowdsourcing methods has resulted in measurable benefits for a human rights organisation by transforming their information and knowledge management using a novel approach that supports human rights monitoring in conflict zones. More specifically, we report on mining and classifying Arabic Twitter in order to identify potential human rights abuse incidents in a continuous stream of social media data within a specified geographical region. Results show deep learning approaches such as LSTM allow us to push the precision close to 85% for this task with an F1-score of 75%. Apart from the scientific insights we also demonstrate the viability of the framework which has been deployed as the Ceasefire Iraq portal for more than three years which has already collected thousands of witness reports from within Iraq. This work is a case study of how progress in artificial intelligence has disrupted even the operation of relatively small-scale organisations