Similarity Assessment through blocking and affordance assignment in Textual CBR

It has been conceived that children learn new objects through their affordances, that is, the actions that can be taken on them. We suggest that web pages also have affordances defined in terms of the users' information need they meet. An assumption of the proposed approach is that different parts of a text may not be equally important / relevant to a given query. Judgment on the relevance of a web document requires, therefore, a thorough look into its parts, rather than treating it as a monolithic content. We propose a method to extract and assign affordances to texts and then use these affordances to retrieve the corresponding web pages. The overall approach presented in the paper relies on case-based representations that bridge the queries to the affordances of web documents. We tested our method on the tourism domain and the results are promising.Comment: 10 pages, 3 figures, WebCBR 2010, Alessandria, Ital

An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression

This paper attempt to study the effectiveness of text representation schemes on two tasks namely: User Aggression and Fact Detection from the social media contents. In User Aggression detection, The aim is to identify the level of aggression from the contents generated in the Social media and written in the English, Devanagari Hindi and Romanized Hindi. Aggression levels are categorized into three predefined classes namely: `Non-aggressive`, `Overtly Aggressive`, and `Covertly Aggressive`. During the disaster-related incident, Social media like, Twitter is flooded with millions of posts. In such emergency situations, identification of factual posts is important for organizations involved in the relief operation. We anticipated this problem as a combination of classification and Ranking problem. This paper presents a comparison of various text representation scheme based on BoW techniques, distributed word/sentence representation, transfer learning on classifiers. Weighted $F_1$ score is used as a primary evaluation metric. Results show that text representation using BoW performs better than word embedding on machine learning classifiers. While pre-trained Word embedding techniques perform better on classifiers based on deep neural net. Recent transfer learning model like ELMO, ULMFiT are fine-tuned for the Aggression classification task. However, results are not at par with pre-trained word embedding model. Overall, word embedding using fastText produce best weighted $F_1$-score than Word2Vec and Glove. Results are further improved using pre-trained vector model. Statistical significance tests are employed to ensure the significance of the classification results. In the case of lexically different test Dataset, other than training Dataset, deep neural models are more robust and perform substantially better than machine learning classifiers.Comment: 21 Page, 2 Figur

Decision Support for e-Governance: A Text Mining Approach

Information and communication technology has the capability to improve the process by which governments involve citizens in formulating public policy and public projects. Even though much of government regulations may now be in digital form (and often available online), due to their complexity and diversity, identifying the ones relevant to a particular context is a non-trivial task. Similarly, with the advent of a number of electronic online forums, social networking sites and blogs, the opportunity of gathering citizens' petitions and stakeholders' views on government policy and proposals has increased greatly, but the volume and the complexity of analyzing unstructured data makes this difficult. On the other hand, text mining has come a long way from simple keyword search, and matured into a discipline capable of dealing with much more complex tasks. In this paper we discuss how text-mining techniques can help in retrieval of information and relationships from textual data sources, thereby assisting policy makers in discovering associations between policies and citizens' opinions expressed in electronic public forums and blogs etc. We also present here, an integrated text mining based architecture for e-governance decision support along with a discussion on the Indian scenario.Comment: 19 Pages, 7 Figure