Explicit Semantic Analysis (ESA) is a technique used to represent a piece of text as a vector in the space of concepts, such as Articles found in Wikipedia. We propose a methodology to incorporate knowledge of Inter-relatedness between Wikipedia Articles to the vectors obtained from ESA using a technique called Retrofitting to improve the performance of subsequent tasks that use ESA to form vector embeddings. Especially we use an undirected Graph to represent this knowledge with nodes as Articles and edges as inter relations between two Articles. Here, we also emphasize how the ESA step could be seen as a predominantly bottom-up approach using a corpus to come up with vector representations and the incorporation of top-down knowledge which is the relations between Articles to further improve it. We test our hypothesis on several smaller subsets of the Wikipedia corpus and show that our proposed methodology leads to decent improvements in performance measures including Spearman's Rank correlation coefficient in most cases.
Insight Centre for Data Analytics（洞察数据分析中心）；National University of Ireland, Galway（爱尔兰国立高威大学）
Many researchers have made use of the Wikipedia network for relatedness and similarity tasks. However, most approaches use only the most recent information and not historical changes in the network. We provide an analysis of entity relatedness using temporal graph-based approaches over different versions of the Wikipedia article link network and DBpedia, which is an open-source knowledge base extracted from Wikipedia. We consider creating the Wikipedia article link network as both a union and intersection of edges over multiple time points and present a novel variation of the Jaccard index to …
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood. In this paper we study the different types of links in Wikipedia, and contrast the use of the full graph with respect to just direct links. We apply a well-known random walk algorithm on two tasks, word relatedness and named-entity disambiguation. We show that using the full graph is more effective than just direct links by a large margin, that non-reciprocal links harm performance, and that there is no benefit from categories and infoboxes, with coherent results on both tasks. We set …
A promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a consultation or to recruit suitable patients for a study. This requires search capabilities far beyond simple string matching, including the retrieval of concepts (diagnoses, symptoms, medications, etc.) related to the one in question. The suitability of AI methods for such applications is tested by predicting the relatedness of concepts with known relatedness scores. However, all existing biomedical concept relatedness …
The Ohio State University（俄亥俄州立大学）；National Institutes of Health（美国国立卫生研究院）；Washington University in St. Louis（圣路易斯华盛顿大学）
Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph …
Institut Télécom – Télécom Bretagne（电信学院——布列塔尼电信学院）；University of Aizu（会津大学）
We describe a new semantic relatedness measure combining the Wikipedia-based Explicit Semantic Analysis measure, the WordNet path measure and the mixed collocation index. Our measure achieves the currently highest results on the WS-353 test: a Spearman rho coefficient of 0.79 (vs. 0.75 in (Gabrilovich and Markovitch, 2007)) when applying the measure directly, and a value of 0.87 (vs. 0.78 in (Agirre et al., 2009)) when using the prediction of a polynomial SVM classifier trained on our measure. In the appendix we discuss the adaptation of ESA to 2011 Wikipedia data, as well as various unsuccess…
University of Aizu（会津大学）；Institut Télécom – Télécom Bretagne（法国电信学院－布列塔尼电信学院）
This paper investigates the efficiency of the EWC semantic relatedness measure in an ad-hoc retrieval task. This measure combines the Wikipedia-based Explicit Semantic Analysis measure, the WordNet path measure and the mixed collocation index. In the experiments, the open source search engine Terrier was utilised as a tool to index and retrieve data. The proposed technique was tested on the NTCIR data collection. The experiments demonstrated promising results.
Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitation, we propose using Wikipedia as a previously untapped source of rich, georeferenced textual information with global coverage. We construct a novel large-scale, multi-modal dataset by pairing geo-referenced Wikipedia articles with satellite imagery of their corresponding locations. To prove the efficacy of this dataset, we focus on the African continent and train a deep network to classify images based on labels ex…
Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset
Computational Approaches to Modeling Language (CAMeL) Lab（计算语言建模方法（CAMeL）实验室）；New York University Abu Dhabi（纽约大学阿布扎克分校）；Ain Shams University（艾因夏姆斯大学）；Stony Brook University（石溪大学）；Department of Artificial Intelligence, University of Malta（人工智能系，马耳他大学）
University of Southern California（南加利福尼亚大学）；Information Sciences Institute, University of Southern California（南加利福尼亚大学信息科学研究所）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a stat…
Carnegie Mellon University（卡内基梅隆大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Washington（华盛顿大学）
Social biases on Wikipedia, a widely-read global platform, could greatly influence public opinion. While prior research has examined man/woman gender bias in biography articles, possible influences of other demographic attributes limit conclusions. In this work, we present a methodology for analyzing Wikipedia pages about people that isolates dimensions of interest (e.g., gender), from other attributes (e.g., occupation). Given a target corpus for analysis (e.g.~biographies about women), we present a method for constructing a comparison corpus that matches the target corpus in as many attribut…
Institut Télécom（电信学院）；Télécom Bretagne（布列塔尼电信学院）；University of Aizu（会津大学）
[This is the translation of paper "Arborification de Wikipédia et analyse sémantique explicite stratifiée" submitted to TALN 2012.] We present an extension of the Explicit Semantic Analysis method by Gabrilovich and Markovitch. Using their semantic relatedness measure, we weight the Wikipedia categories graph. Then, we extract a minimal spanning tree, using Chu-Liu & Edmonds' algorithm. We define a notion of stratified tfidf where the stratas, for a given Wikipedia page and a given term, are the classical tfidf and categorical tfidfs of the term in the ancestor categories of the page (ancestor…
Studio Ousia；RIKEN AIP（理化学研究所先进智能项目中心）；University of Washington（华盛顿大学）；The University of Tokyo（东京大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；National Institute of Informatics（国立情报学研究所）；Keio University（庆应义塾大学）
The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool…
University of Würzburg（维尔茨堡大学）；L3S Research Center Hanover（汉诺威L3S研究中心）
Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as expl…
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 W…
Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerica…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Mined Semantic Analysis (MSA) is a novel concept space model which employs unsupervised learning to generate semantic representations of text. MSA represents textual structures (terms, phrases, documents) as a Bag of Concepts (BoC) where concepts are derived from concept rich encyclopedic corpora. Traditional concept space models exploit only target corpus content to construct the concept space. MSA, alternatively, uncovers implicit relations between concepts by mining for their associations (e.g., mining Wikipedia's "See also" link graph). We evaluate MSA's performance on benchmark datasets f…
Institut Mines-Télécom（矿业电信研究所）；University of Aizu（会津大学）
We present an extended, thematically reinforced version of Gabrilovich and Markovitch's Explicit Semantic Analysis (ESA), where we obtain thematic information through the category structure of Wikipedia. For this we first define a notion of categorical tfidf which measures the relevance of terms in categories. Using this measure as a weight we calculate a maximal spanning tree of the Wikipedia corpus considered as a directed graph of pages and categories. This tree provides us with a unique path of "most related categories" between each page and the top of the hierarchy. We reinforce tfidf of …
Innovation Exchange（创新交流中心）；IBM Ireland（爱尔兰国际商业机器公司）；ADAPT Research Centre（ADAPT研究中心）；Technological University Dublin（都柏林理工大学）；Underwriters Laboratories（美国保险商实验室）
This paper connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical representations encode different forms of relatedness. A particularly important distinction within semantic relatedness is that of thematic versus taxonomic relatedness. Next, we present a number of experiments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. These experiments demonstrate how the properties of the synthetic corp…
We propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning, to counteract the lack of resources for evaluating computational models of lexical semantic change. Our framework exploits an intuitive notion of semantic relatedness, and distinguishes between innovative and reductive meaning changes with high inter-annotator agreement. The resulting test set for German comprises ratings from five annotators for the relatedness of 1,320 use pairs across 22 target words.
South China University of Technology（华南理工大学）；Research Institute of SCUT in Yangjiang（华南理工大学阳江研究院）
Stack Overflow is one of the most popular Programming Community-based Question Answering (PCQA) websites that has attracted more and more users in recent years. When users raise or inquire questions in Stack Overflow, providing related questions can help them solve problems. Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited since interaction information between two questions may be lost. In this paper, we adopt the deep learning technique, propose an Attention-based Sentence pair Interaction …
University of Tennessee（田纳西大学）；Kent State University（肯特州立大学）
News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topi…
In this paper we present a novel method for retrieving information in languages other than that of the query. We use this technique in combination with existing traditional Cross Language Information Retrieval (CLIR) techniques to improve their results. This method has a number of advantages over traditional techniques that rely on machine translation to translate the query and then search the target document space using a machine translation. This method is not limited to the availability of a machine translation algorithm for the desired language and uses already existing sources of readily …
Wikipedia is nowadays a widely used encyclopedia, and one of the most visible sites on the Internet. Its strong principle of collaborative work and free editing sometimes generates disputes due to disagreements between users. In this article we study how the wikipedian community resolves the conflicts and which roles do wikipedian choose in this process. We observed the users behavior both in the article talk pages, and in the Arbitration Committee pages specifically dedicated to serious disputes. We first set up a users typology according to their involvement in conflicts and their publishing…
Factual Inconsistencies in Multilingual Wikipedia Tables
CNR ISTC（意大利国家研究委员会信息与通信技术研究所）；Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所）；Tallinn University of Technology（塔林理工大学）；EURECOM（欧瑞康）；Technical University of Munich（慕尼黑技术大学）；University of Amsterdam（阿姆斯特丹大学）
everis / NTT Data（埃维里斯/恩梯梯数据）；Universitat Politècnica de València（瓦伦西亚理工大学）；University of Zaragoza（萨拉戈萨大学）；Aragon Institute of Engineering Research (I3A)（阿拉贡工程研究所）
Understanding the meaning of words is crucial for many tasks that involve human-machine interaction. This has been tackled by research in Word Sense Disambiguation (WSD) in the Natural Language Processing (NLP) field. Recently, WSD and many other NLP tasks have taken advantage of embeddings-based representation of words, sentences, and documents. However, when it comes to WSD, most embeddings models suffer from ambiguity as they do not capture the different possible meanings of the words. Even when they do, the list of possible meanings for a word (sense inventory) has to be known in advance a…
L3S Research Center（L3S研究中心）；University of Southampton（南安普顿大学）
Wikipedia articles representing an entity or a topic in different language editions evolve independently within the scope of the language-specific user communities. This can lead to different points of views reflected in the articles, as well as complementary and inconsistent information. An analysis of how the information is propagated across the Wikipedia language editions can provide important insights in the article evolution along the temporal and cultural dimensions and support quality control. To facilitate such analysis, we present MultiWiki - a novel web-based user interface that prov…
L3S Research Center（L3S研究中心）；University of Southampton（南安普顿大学）
In this article we address the problem of text passage alignment across interlingual article pairs in Wikipedia. We develop methods that enable the identification and interlinking of text passages written in different languages and containing overlapping information. Interlingual text passage alignment can enable Wikipedia editors and readers to better understand language-specific context of entities, provide valuable insights in cultural differences and build a basis for qualitative analysis of the articles. An important challenge in this context is the trade-off between the granularity of th…
Insight Centre for Data Analytics（数据分析洞察中心）；University College Dublin（都柏林大学）
We present an unsupervised explainable word embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a word using human-readable labels, thereby it readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed word embedding model, we consider its usefulness in three fundamental tasks: 1) intruder detection - to evaluate its ability to identify a non-cohere…
UvA Amsterdam（阿姆斯特丹大学）；Politecnico di Milano（米兰理工大学）；Barcelona Media（巴塞罗那媒体中心）；Sciences Politiques（巴黎政治学院）
Collaborative content creation inevitably reaches situations where different points of view lead to conflict. We focus on Wikipedia, the free encyclopedia anyone may edit, where disputes about content in controversial articles often reflect larger societal debates. While Wikipedia has a public edit history and discussion section for every article, the substance of these sections is difficult to phantom for Wikipedia users interested in the development of an article and in locating which topics were most controversial. In this paper we present Contropedia, a tool that augments Wikipedia article…
