7,881 research outputs found
Using an ontology to improve the web search experience
The search terms that a user passes to a search engine are often ambiguous, referring to homonyms. The results in these cases are a mixture of links to documents that contain different meanings of the search terms. Current search engines provide suggested query completions in a dropdown list. However, such lists are not well organized, mixing completions for different meanings. In addition, the suggested search phrases are not discriminating enough. Moreover, current search engines often return an unexpected number of results. Zero hits are naturally undesirable, while too many hits are likely to be overwhelming and of low precision.
This dissertation work aims at providing a better Web search experience for the users by addressing the above described problems.To improve the search for homonyms, suggested completions are well organized and visually separated. In addition, this approach supports the use of negative terms to disambiguate the suggested completions in the list. The dissertation presents an algorithm to generate the suggested search completion terms using an ontology and new ways of displaying homonymous search results. These algorithms have been implemented in the Ontology-Supported Web Search (OSWS) System for famous people.
This dissertation presents a method for dynamically building the necessary ontology of famous people based on mining the suggested completions of a search engine. This is combined with data from DBpedia. To enhance the OSWS ontology, Facebook is used as a secondary data source. Information from people public pages is mined and Facebook attributes are cleaned up and mapped to the OSWS ontology.
To control the size of the result sets returned by the search engines, this dissertation demonstrates a query rewriting method for generating alternative query strings and implements a model for predicting the number of search engine hits for each alternative query string, based on the English language frequencies of the words in the search terms. Evaluation experiments of the hit count prediction model are presented for three major search engines. The dissertation also discusses and quantifies how far the Google, Yahoo! and Bing search engines diverge from monotonic behavior, considering negative and positive search terms separately
Automated Detection of Usage Errors in non-native English Writing
In an investigation of the use of a novelty detection algorithm for identifying inappropriate word
combinations in a raw English corpus, we employ an
unsupervised detection algorithm based on the one-
class support vector machines (OC-SVMs) and extract
sentences containing word sequences whose frequency
of appearance is significantly low in native English
writing. Combined with n-gram language models and
document categorization techniques, the OC-SVM classifier assigns given sentences into two different
groups; the sentences containing errors and those
without errors. Accuracies are 79.30 % with bigram
model, 86.63 % with trigram model, and 34.34 % with four-gram model
The Google Similarity Distance
Words and phrases acquire meaning from the way they are used in society, from
their relative semantics to other words and phrases. For computers the
equivalent of `society' is `database,' and the equivalent of `use' is `way to
search the database.' We present a new theory of similarity between words and
phrases based on information distance and Kolmogorov complexity. To fix
thoughts we use the world-wide-web as database, and Google as search engine.
The method is also applicable to other search engines and databases. This
theory is then applied to construct a method to automatically extract
similarity, the Google similarity distance, of words and phrases from the
world-wide-web using Google page counts. The world-wide-web is the largest
database on earth, and the context information entered by millions of
independent users averages out to provide automatic semantics of useful
quality. We give applications in hierarchical clustering, classification, and
language translation. We give examples to distinguish between colors and
numbers, cluster names of paintings by 17th century Dutch masters and names of
books by English novelists, the ability to understand emergencies, and primes,
and we demonstrate the ability to do a simple automatic English-Spanish
translation. Finally, we use the WordNet database as an objective baseline
against which to judge the performance of our method. We conduct a massive
randomized trial in binary classification using support vector machines to
learn categories based on our Google distance, resulting in an a mean agreement
of 87% with the expert crafted WordNet categories.Comment: 15 pages, 10 figures; changed some text/figures/notation/part of
theorem. Incorporated referees comments. This is the final published version
up to some minor changes in the galley proof
Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information
In computing, spell checking is the process of detecting and sometimes
providing spelling suggestions for incorrectly spelled words in a text.
Basically, a spell checker is a computer program that uses a dictionary of
words to perform spell checking. The bigger the dictionary is, the higher is
the error detection rate. The fact that spell checkers are based on regular
dictionaries, they suffer from data sparseness problem as they cannot capture
large vocabulary of words including proper names, domain-specific terms,
technical jargons, special acronyms, and terminologies. As a result, they
exhibit low error detection rate and often fail to catch major errors in the
text. This paper proposes a new context-sensitive spelling correction method
for detecting and correcting non-word and real-word errors in digital text
documents. The approach hinges around data statistics from Google Web 1T 5-gram
data set which consists of a big volume of n-gram word sequences, extracted
from the World Wide Web. Fundamentally, the proposed method comprises an error
detector that detects misspellings, a candidate spellings generator based on a
character 2-gram model that generates correction suggestions, and an error
corrector that performs contextual error correction. Experiments conducted on a
set of text documents from different domains and containing misspellings,
showed an outstanding spelling error correction rate and a drastic reduction of
both non-word and real-word errors. In a further study, the proposed algorithm
is to be parallelized so as to lower the computational cost of the error
detection and correction processes.Comment: LACSC - Lebanese Association for Computational Sciences -
http://www.lacsc.or
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
Peptide vocabulary analysis reveals ultra-conservation and homonymity in protein sequences
A new algorithm is presented for vocabulary analysis (word detection) in texts of human origin. It performs at 60%–70% overall accuracy and greater than 80% accuracy for longer words, and approximately 85% sensitivity on Alice in Wonderland, a considerable improvement on previous methods. When applied to protein sequences, it detects short sequences analogous to words in human texts, i.e. intolerant to changes in spelling (mutation), and relatively contextindependent in their meaning (function). Some of these are homonyms of up to 7 amino acids, which can assume different structures in different proteins. Others are ultra-conserved stretches of up to 18 amino acids within proteins of less than 40% overall identity, reflecting extreme constraint or convergent evolution. Different species are found to have qualitatively different major peptide vocabularies, e.g. some are dominated by large gene families, while others are rich in simple repeats or dominated by internally repetitive proteins. This suggests the possibility of a peptide vocabulary signature, analogous to genome signatures in DNA. Homonyms may be useful in detecting convergent evolution and positive selection in protein evolution. Ultra-conserved words may be useful in identifying structures intolerant to substitution over long periods of evolutionary time
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