Real Time Web Search Framework for Performing Efficient Retrieval of Data

With the rapidly growing amount of information on the internet, real-time system is one of the key strategies to cope with the information overload and to help users in finding highly relevant information. Real-time events and domain-specific information are important knowledge base references on the Web that frequently accessed by millions of users. Real-time system is a vital to product and a technique must resolve the context of challenges to be more reliable, e.g. short data life-cycles, heterogeneous user interests, strict time constraints, and context-dependent article relevance. Since real-time data have only a short time to live, real-time models have to be continuously adapted, ensuring that real-time data are always up-to-date. The focal point of this manuscript is for designing a real-time web search approach that aggregates several web search algorithms at query time to tune search results for relevancy. We learn a context-aware delegation algorithm that allows choosing the best real-time algorithms for each query request. The evaluation showed that the proposed approach outperforms the traditional models, in which it allows us to adapt the specific properties of the considered real-time resources. In the experiments, we found that it is highly relevant for most recently searched queries, consistent in its performance, and resilient to the drawbacks faced by other algorithms

Detecting Locations from Twitter Messages Invited Talk

Abstract There is a large amount of information that can be extracted automatically from social media messages. Of particular interest are the topics discussed by the users, the opinions and emotions expressed, and the events and the locations mentioned. This work focuses on machine learning methods for detecting locations from Twitter messages, because the extracted locations can be useful in business, marketing and defence applications . There are two types of locations that we are interested in: location entities mentioned in the text of each message and the physical locations of the users. For the first type of locations (task 1), we detected expressions that denote locations and we classified them into names of cities, provinces/states, and countries. We approached the task in a novel way, consisting in two stages. In the first stage, we trained Conditional Random Field models with various sets of features. We collected and annotated our own dataset for training and testing. In the second stage, we resolved cases when more than one place with the same name exists, by applying a set of heuristics . For the second type of locations (task 2), we put together all the tweets written by a user, in order to predict his/her physical location. Only a few users declare their locations in their Twitter profiles, but this is sufficient to automatically produce training and test data for our classifiers. We experimented with two existing datasets collected from users located in the U.S. We propose a deep learning architecture for the solving the task, because deep learning was shown to work well for other natural language processing tasks, and because standard classifiers were already tested for the user location task. We designed a model that predicts the U.S. region of the user and his/her U.S. state, and another model that predicts the longitude and latitude of the user's location. We found that stacked denoising autoencoders are well suited for this task, with results comparable to the state-of-the-art

Investigating cross-language speech retrieval for a spontaneous conversational speech collection

Cross-language retrieval of spontaneous speech combines the challenges of working with noisy automated transcription and language translation. The CLEF 2005 Cross-Language Speech Retrieval (CL-SR) task provides a standard test collection to investigate these challenges. We show that we can improve retrieval performance: by careful selection of the term weighting scheme; by decomposing automated transcripts into phonetic substrings to help ameliorate transcription errors; and by combining automatic transcriptions with manually-assigned metadata. We further show that topic translation with online machine translation resources yields effective CL-SR

A New Approach of Intelligent Data Retrieval Paradigm

What is a real time agent, how does it remedy ongoing daily frustrations for users, and how does it improve the retrieval performance in World Wide Web? These are the main question we focus on this manuscript. In many distributed information retrieval systems, information in agents should be ranked based on a combination of multiple criteria. Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness. Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined. The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination. Different ways for ranking agents make this strategy difficult. In this research, we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time. The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes. The proposed approach was tested on the queries provided by Text Retrieval Conference (TREC). Experimental results showed that our approach is effective and robust compared with offline search platforms

Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201

Text Segmentation Using Roget-Based Weighted Lexical Chains

In this article we present a new method for text segmentation. The method relies on the number of lexical chains (LCs) which end in a sentence, which begin in the following sentence and which traverse the two successive sentences. The lexical chains are based on Roget's thesaurus (the 1987 and the 1911 version). We evaluate the method on ten texts from the DUC 2002 conference and on twenty texts from the CAST project corpus, using a manual segmentation as gold standard