1,856 research outputs found
Part of Speech Based Term Weighting for Information Retrieval
Automatic language processing tools typically assign to terms so-called
weights corresponding to the contribution of terms to information content.
Traditionally, term weights are computed from lexical statistics, e.g., term
frequencies. We propose a new type of term weight that is computed from part of
speech (POS) n-gram statistics. The proposed POS-based term weight represents
how informative a term is in general, based on the POS contexts in which it
generally occurs in language. We suggest five different computations of
POS-based term weights by extending existing statistical approximations of term
information measures. We apply these POS-based term weights to information
retrieval, by integrating them into the model that matches documents to
queries. Experiments with two TREC collections and 300 queries, using TF-IDF &
BM25 as baselines, show that integrating our POS-based term weights to
retrieval always leads to gains (up to +33.7% from the baseline). Additional
experiments with a different retrieval model as baseline (Language Model with
Dirichlet priors smoothing) and our best performing POS-based term weight, show
retrieval gains always and consistently across the whole smoothing range of the
baseline
Information Retrieval Models
Many applications that handle information on the internet would be completely\ud
inadequate without the support of information retrieval technology. How would\ud
we find information on the world wide web if there were no web search engines?\ud
How would we manage our email without spam filtering? Much of the development\ud
of information retrieval technology, such as web search engines and spam\ud
filters, requires a combination of experimentation and theory. Experimentation\ud
and rigorous empirical testing are needed to keep up with increasing volumes of\ud
web pages and emails. Furthermore, experimentation and constant adaptation\ud
of technology is needed in practice to counteract the effects of people that deliberately\ud
try to manipulate the technology, such as email spammers. However,\ud
if experimentation is not guided by theory, engineering becomes trial and error.\ud
New problems and challenges for information retrieval come up constantly.\ud
They cannot possibly be solved by trial and error alone. So, what is the theory\ud
of information retrieval?\ud
There is not one convincing answer to this question. There are many theories,\ud
here called formal models, and each model is helpful for the development of\ud
some information retrieval tools, but not so helpful for the development others.\ud
In order to understand information retrieval, it is essential to learn about these\ud
retrieval models. In this chapter, some of the most important retrieval models\ud
are gathered and explained in a tutorial style
Learning to detect video events from zero or very few video examples
In this work we deal with the problem of high-level event detection in video.
Specifically, we study the challenging problems of i) learning to detect video
events from solely a textual description of the event, without using any
positive video examples, and ii) additionally exploiting very few positive
training samples together with a small number of ``related'' videos. For
learning only from an event's textual description, we first identify a general
learning framework and then study the impact of different design choices for
various stages of this framework. For additionally learning from example
videos, when true positive training samples are scarce, we employ an extension
of the Support Vector Machine that allows us to exploit ``related'' event
videos by automatically introducing different weights for subsets of the videos
in the overall training set. Experimental evaluations performed on the
large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness
of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for
publicatio
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Lexical Query Modeling in Session Search
Lexical query modeling has been the leading paradigm for session search. In
this paper, we analyze TREC session query logs and compare the performance of
different lexical matching approaches for session search. Naive methods based
on term frequency weighing perform on par with specialized session models. In
addition, we investigate the viability of lexical query models in the setting
of session search. We give important insights into the potential and
limitations of lexical query modeling for session search and propose future
directions for the field of session search.Comment: ICTIR2016, Proceedings of the 2nd ACM International Conference on the
Theory of Information Retrieval. 201
- …