Using Explicit Semantic Analysis for Cross-Lingual Link Discovery

This paper explores how to automatically generate cross language links between resources in large document collections. The paper presents new methods for Cross Lingual Link Discovery(CLLD) based on Explicit Semantic Analysis (ESA). The methods are applicable to any multilingual document collection. In this report, we present their comparative study on the Wikipedia corpus and provide new insights into the evaluation of link discovery systems. In particular, we measure the agreement of human annotators in linking articles in different language versions of Wikipedia, and compare it to the results achieved by the presented methods

The meaning of negation in the second language classroom: evidence from 'any'

This article brings together an experimental study involving L2 knowledge of negation in English and an analysis of how English language textbooks treat negation, in order to consider whether textbook explanations of negation could better exploit linguistic insights into negation. We focus on the English negative polarity item any, whose distribution is contingent on negation, whether through the explicit negator not or through lexical semantic negators (e.g. hardly). Our experiment compares Chinese-speaking learners with existing data from Arabic-speaking learners, finding lower accuracy on any with lexical semantic negators in both groups. Our textbook analysis reveals an approach to negation that is limited to form, focusing on the explicit negator not without explicit treatment of other types of negation. We propose that emphasizing the meaning of negation, with explicit treatment of the full range of negative forms could facilitate more complete acquisition across a range of grammatical properties where negation plays a role

An Improved Web Explorer using Explicit Semantic Similarity with ontology and TF-IDF Approach

The Improved Web Explorer aims at extraction and selection of the best possible hyperlinks and retrieving more accurate search results for the entered search query. The hyperlinks that are more preferable to the entered search query are evaluated by taking into account weighted values of frequencies of words in search string that are present in anchor texts and plain texts available in title and body tags of various hyperlink pages respectively to retrieve relevant hyperlinks from all available links. Then the concept of ontology is used to gain insights of words in search string by finding their hypernyms, hyponyms and synsets to reach to the source and context of the words in search string. The Explicit Semantic Similarity analysis along with Naïve Bayes method is used to find the semantic similarity between lexically different terms using Wikipedia and Google as explicit semantic analysis tools and calculating the probabilities of occurrence of words in anchor and body texts .Vector Space Model is being used to calculate Term frequency and Inverse document frequency values, and then calculate cosine similarities between the entered Search query and extracted relevant hyperlinks to get the most appropriate relevance wise ranked search results to the entered search string

Supporting decision making process with "Ideal" software agents: what do business executives want?

According to Simon’s (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives’ perceptions of using agent-based support systems and the criteria for design and development of their “ideal” intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of “ideal” agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives’ perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users’ point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation

Structural Regularities in Text-based Entity Vector Spaces

Entity retrieval is the task of finding entities such as people or products in response to a query, based solely on the textual documents they are associated with. Recent semantic entity retrieval algorithms represent queries and experts in finite-dimensional vector spaces, where both are constructed from text sequences. We investigate entity vector spaces and the degree to which they capture structural regularities. Such vector spaces are constructed in an unsupervised manner without explicit information about structural aspects. For concreteness, we address these questions for a specific type of entity: experts in the context of expert finding. We discover how clusterings of experts correspond to committees in organizations, the ability of expert representations to encode the co-author graph, and the degree to which they encode academic rank. We compare latent, continuous representations created using methods based on distributional semantics (LSI), topic models (LDA) and neural networks (word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as doc2vec and SERT, systematically perform better at clustering than LSI, LDA and word2vec. When it comes to encoding entity relations, SERT performs best.Comment: ICTIR2017. Proceedings of the 3rd ACM International Conference on the Theory of Information Retrieval. 201

Footprints of information foragers: Behaviour semantics of visual exploration

Social navigation exploits the knowledge and experience of peer users of information resources. A wide variety of visual–spatial approaches become increasingly popular as a means to optimize information access as well as to foster and sustain a virtual community among geographically distributed users. An information landscape is among the most appealing design options of representing and communicating the essence of distributed information resources to users. A fundamental and challenging issue is how an information landscape can be designed such that it will not only preserve the essence of the underlying information structure, but also accommodate the diversity of individual users. The majority of research in social navigation has been focusing on how to extract useful information from what is in common between users' profiles, their interests and preferences. In this article, we explore the role of modelling sequential behaviour patterns of users in augmenting social navigation in thematic landscapes. In particular, we compare and analyse the trails of individual users in thematic spaces along with their cognitive ability measures. We are interested in whether such trails can provide useful guidance for social navigation if they are embedded in a visual–spatial environment. Furthermore, we are interested in whether such information can help users to learn from each other, for example, from the ones who have been successful in retrieving documents. In this article, we first describe how users' trails in sessions of an experimental study of visual information retrieval can be characterized by Hidden Markov Models. Trails of users with the most successful retrieval performance are used to estimate parameters of such models. Optimal virtual trails generated from the models are visualized and animated as if they were actual trails of individual users in order to highlight behavioural patterns that may foster social navigation. The findings of the research will provide direct input to the design of social navigation systems as well as to enrich theories of social navigation in a wider context. These findings will lead to the further development and consolidation of a tightly coupled paradigm of spatial, semantic and social navigation

Conversation with Robert Brandom

In this broad interview Robert Brandom talks about many themes concerning his work and about his career and education. Brandom reconstructs the main debts that he owes to colleagues and teachers, especially Wilfrid Sellars, Richard Rorty, and David Lewis, and talks about the projects he’s currently working on. He also talks about contemporary and classical pragmatism, and of the importance of classical thinkers like Kant and Hegel for contemporary debates. Other themes go deeper into the principal topics of his theoretical work – in particular, his later understanding of expressivism, his take on the debate between representationalists and anti-representationalists in semantics, the main open problems for his wide inferentialist project, and his methodological preference for the normative vocabulary in his account of discursive practice. Finally, Brandom touches on the epistemic role of perception and on his views about the importance of the phenomenological aspects of perceptual experience

Visualising and animating visual information foraging in context

Optimal information foraging provides a potentially useful framework for modelling, analysing, and interpreting search strategies of users through a spatial-semantic interface. Improving the understanding of behavioural patterns of users in such environments has implications for the design and refinement of a range of user interfaces. In this article, we outline the role of optimal information foraging in the study of visual information retrieval and how one may use visualisation and animation techniques to put behavioural patterns in context. Behavioural patterns of information foraging in an information space are visualised and animated to aid further in-depth analysis of search strategies