Deep Neural Network Structure to Improve Individual Performance based Author Classification

This paper proposed an improved method for author name disambiguation problem, both homonym and synonym. The data prepared is the distance data of each pair of author’s attributes, Levenshtein distance are used. Using Deep Neural Networks, we found large gains on performance. The result shows that level of accuracy is 99.6% with a low number of hidden layer

Applying Wikipedia to Interactive Information Retrieval

There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval

Privacy-preserving document similarity detection

The document similarity detection is an important technique used in many applications. The existence of the tool that guarantees the privacy protection of the documents during the comparison will expand the area where this technique can be applied. The goal of this project is to develop a method for privacy-preserving document similarity detection capable to identify either semantically or syntactically similar documents. As the result two methods were designed, implemented, and evaluated. In the first method privacy-preserving data comparison protocol was applied for secure comparison. This original protocol was created as a part of this thesis. In the second method modified private-matching scheme was used. In both methods the Natural Language processing techniques were utilized to capture the semantic relations between documents. During the testing phase the first method was found to be too slow for the practical application. The second method, on the contrary, was rather fast and effective. It can be used for creation of the tool for detecting syntactical and semantic similarity in a privacy-preserving way

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing

Global Keyword Tracking in Archaeology

With the digitization of information, discoveries of events that previously took much human effort can now be found automatically. As example, we investigate several scandals in the art and antiques area that occurred between 1985 and 2005. In these events, the auction house Sotheby's was suspected to accept or even help the trading of smuggled paintings or antiques and the famous Getty Museum was exposed as purchasing antiques linked to treasure hunters. Discovering these secrets required the hard work of journalists, detectives, TV producers, and so on. The investigators were involved in illegal trades and various dangerous situations during their process of investigation. In comparison, today, with the access to digital version of large datasets, we are able to discover similar events using computationally-based techniques without the high risk and the cost of human labour needed before. This thesis introduces our tool for extracting keywords, terms and peoples' names from news articles, books, and marking them on an interactive map. We use the New York Times as the main resource, extract location terms in each news articles using Gazetteer, extract keywords and people's names in each articles and reduce ambiguity using WordNet. Combining them, we are able to form location-keyword-time pairs for each articles, and together they form a database. Then we build an interactive map based on the database. The map is able to show the relationships between location and keywords. The linkages between two or more people or locations is able to show on the map. The demonstration was able to perform similar detection process as those journalists did in the late 90s. The paper also introduces additional findings during the examination of the original datasets. As a news media outlet based in New York, we see evidence that the New York Times turns out to focus much more on New York City and the United States compared with other countries. With the extraction of locations inside the articles, we were able to see the distribution of articles mentioning different countries differs a lot when comparing the different continents. Our visualization also shows how locations names were changed throughout time, and how the terms people use describing a certain object changes