54,484 research outputs found
Exploring Topic-based Language Models for Effective Web Information Retrieval
The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the .GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model
Conceptual Spaces in Object-Oriented Framework
The aim of this paper is to show that the middle level of
mental representations in a conceptual spaces framework is consistent
with the OOP paradigm. We argue that conceptual spaces framework
together with vague prototype theory of categorization appears to be
the most suitable solution for modeling the cognitive apparatus of
humans, and that the OOP paradigm can be easily and intuitively
reconciled with this framework. First, we show that the prototypebased
OOP approach is consistent with Gärdenfors’ model in terms
of structural coherence. Second, we argue that the product of cloning
process in a prototype-based model is in line with the structure of
categories in Gärdenfors’ proposal. Finally, in order to make the fuzzy
object-oriented model consistent with conceptual space, we
demonstrate how to define membership function in a more cognitive
manner, i.e. in terms of similarity to prototype
Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings
In this paper we present a novel interactive multimodal learning system,
which facilitates search and exploration in large networks of social multimedia
users. It allows the analyst to identify and select users of interest, and to
find similar users in an interactive learning setting. Our approach is based on
novel multimodal representations of users, words and concepts, which we
simultaneously learn by deploying a general-purpose neural embedding model. We
show these representations to be useful not only for categorizing users, but
also for automatically generating user and community profiles. Inspired by
traditional summarization approaches, we create the profiles by selecting
diverse and representative content from all available modalities, i.e. the
text, image and user modality. The usefulness of the approach is evaluated
using artificial actors, which simulate user behavior in a relevance feedback
scenario. Multiple experiments were conducted in order to evaluate the quality
of our multimodal representations, to compare different embedding strategies,
and to determine the importance of different modalities. We demonstrate the
capabilities of the proposed approach on two different multimedia collections
originating from the violent online extremism forum Stormfront and the
microblogging platform Twitter, which are particularly interesting due to the
high semantic level of the discussions they feature
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