Observing Interaction: An Introduction to Sequential Analysis, Roger Bakeman, John M. Gottman. Cambridge University Press, Cambridge (1986), ixv, +221. Price £25.00 hardback, £7.95 paperback

Link prediction is an important task for analying social networks which also has applications in other domains like, information retrieval, bioinformatics and e-commerce There exist a variety of techniques for link prediction, ranging from feature-based classification and kernel-based method to matrix factorization and probabilistic graphical models These methods differ from each other with respect to model complexity, prediction performance, scalability, and generalization ability In this article, we survey some representative link prediction methods by categorizing them by the type of the models We largely consider three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model Second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models And, finally the linear algebraic approach which computes the similarity between the nodes in a network by rank-reduced similarity matrices We discuss various existing link prediction models that fall in these broad categories and analyze their strength and weakness We conclude the survey with a discussion on recent developments and future research direction

A Survey of Link Prediction in Social Networks

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

/pdf/wikipedia-based-semantic-interpretation-for-natural-language-4ikazfyj50.pdf

Wikipedia-based semantic interpretation for natural language processing

Berners-Lee's compelling vision of a Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method - creating enough structured data to motivate the development of applications. This paper argues that autonomously "Semantifying Wikipedia" is the best way to solve the problem. We choose Wikipedia as an initial data source, because it is comprehensive, not too large, high-quality, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. We identify several types of structures which can be automatically enhanced in Wikipedia (e.g., link structure, taxonomic data, infoboxes, etc.), and we describea prototype implementation of a self-supervised, machine learning system which realizes our vision. Preliminary experiments demonstrate the high precision of our system's extracted data - in one case equaling that of humans.

/pdf/autonomously-semantifying-wikipedia-274j7gdolk.pdf

Autonomously semantifying wikipedia

Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy. This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.

/pdf/collective-entity-linking-in-web-text-a-graph-based-method-mb9u3pldk3.pdf

Collective entity linking in web text: a graph-based method

In this paper we address the problem of discovering missing hypertext links in Wikipedia. The method we propose consists of two steps: first, we compute a cluster of highly similar pages around a given page, and then we identify candidate links from those similar pages that might be missing on the given page. The main innovation is in the algorithm that we use for identifying similar pages, LTRank, which ranks pages using co-citation and page title information. Both LTRank and the link discovery method are manually evaluated and show acceptable results, especially given the simplicity of the methods and conservativeness of the evaluation criteria.

/pdf/discovering-missing-links-in-wikipedia-1rhp93ie2p.pdf

Discovering missing links in Wikipedia

We seek to improve the robustness and portability of temporal
information extraction systems by incorporating data-driven
techniques. We present two sets of experiments pointing us in this
direction. The first shows that machine-learning-based
recognition of temporal expressions not only achieves high
accuracy on its own but can also improve rule-based
normalization. The second makes use of a staged
normalization architecture to experiment with machine learned
classifiers for certain disambiguation sub-tasks within the
normalization task.

/pdf/towards-task-based-temporal-extraction-and-recognition-3df68wkr90.pdf

Towards Task-Based Temporal Extraction and Recognition

We applied Conditional Random Fields (CRFs) to the tasks of Amharic word segmentation and POS tagging using a small annotated corpus of 1000 words Given the size of the data and the large number of unknown words in the test corpus (80%), an accuracy of 84% for Amharic word segmentation and 74% for POS tagging is encouraging, indicating the applicability of CRFs for a morphologically complex language like Amharic

/pdf/part-of-speech-tagging-for-amharic-using-conditional-random-1vf3ak2dle.pdf

Part of Speech Tagging for Amharic using Conditional Random Fields

This paper describes our participation in the WiQA 2006 pilot on question answering using Wikipedia We present an analysis of the results of our system for both monolingual and bilingual runs The system currently works for Dutch and English

/pdf/the-university-of-amsterdam-at-wiqa-2006-fsnowlrgeb.pdf

The University of Amsterdam at WiQA 2006

We address the task of extracting focused salient information items, relevant and important for a given topic, from a large encyclopedic resource. Specifically, for a given topic (a Wikipedia article) we identify snippets from other articles in Wikipedia that contain important information for the topic of the original article, without duplicates. We compare several methods for addressing the task, and find that a mixture of content-based, link-based, and layout-based features outperforms other methods, especially in combination with the use of so-called reference corpora that capture the key properties of entities of a common type.

/pdf/fact-discovery-in-wikipedia-38dxav6f8c.pdf

Sisay Fissaha Adafre

Papers

Discovering missing links in Wikipedia

Towards Task-Based Temporal Extraction and Recognition

Part of Speech Tagging for Amharic using Conditional Random Fields

The University of Amsterdam at WiQA 2006

Fact Discovery in Wikipedia