828 research outputs found
Extending weighting models with a term quality measure
Weighting models use lexical statistics, such as term frequencies, to derive term weights, which are used to estimate the relevance of a document to a query. Apart from the removal of stopwords, there is no other consideration of the quality of words that are being ‘weighted’. It is often assumed that term frequency is a good indicator for a decision to be made as to how relevant a document is to a query. Our intuition is that raw term frequency could be enhanced to better discriminate between terms. To do so, we propose using non-lexical features to predict the ‘quality’ of words, before they are weighted for retrieval. Specifically, we show how parts of speech (e.g. nouns, verbs) can help estimate how informative a word generally is, regardless of its relevance to a query/document. Experimental results with two standard TREC collections show that integrating the proposed term quality to two established weighting models enhances retrieval performance, over a baseline that uses the original weighting models, at all times
Evaluation of probabilistic models for word frequency and information retrieval
Volume 2 Issue 9 (September 2014
Probability models for information retrieval based on divergence from randomness
This thesis devises a novel methodology based on probability theory, suitable for the construction of term-weighting models of Information Retrieval. Our term-weighting functions are created within a general framework made up of three components. Each of the three components is built independently from the others. We obtain the term-weighting functions from the general model in a purely theoretic way instantiating each component with different probability distribution forms.
The thesis begins with investigating the nature of the statistical inference involved in Information Retrieval. We explore the estimation problem underlying the process of sampling. De Finetti’s theorem is used to show how to convert the frequentist approach into Bayesian inference and we display and employ the derived estimation techniques in the context of Information Retrieval.
We initially pay a great attention to the construction of the basic sample spaces of Information Retrieval. The notion of single or multiple sampling from different populations in the context of Information Retrieval is extensively discussed and used through-out the thesis. The language modelling approach and the standard probabilistic model are studied under the same foundational view and are experimentally compared to the divergence-from-randomness approach.
In revisiting the main information retrieval models in the literature, we show that even language modelling approach can be exploited to assign term-frequency normalization to the models of divergence from randomness. We finally introduce a novel framework for the query expansion. This framework is based on the models of divergence-from-randomness and it can be applied to arbitrary models of IR, divergence-based, language modelling and probabilistic models included. We have done a very large number of experiment and results show that the framework generates highly effective Information Retrieval models
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
Creating Maps of Science Using Topic Models : A Reproducibility Study
Maps of science, or cartography of scientific fields, provide insights into the state of scientific knowledge. Analogous to geographical maps, maps of science present the fields as positions and show the paths connecting each other, which can serve as an intuitive illustration for the history of science or a hint to spot potential opportunities for collaboration. In this work, I investigate the reproducibility of a method to generate such maps. The idea of the method is to derive representations representations for the given scientific fields with topic models and then perform hierarchical clustering on these, which in the end yields a tree of scientific fields as the map. The result is found unreproducible, as my result obtained on the arXiv data set (~130k articles from arXiv Computer Science) shows an inconsistent structure from the one in the reference study. To investigate the cause of the inconsistency, I derive a second set of maps using the same method and an adjusted data set, which is constructed by re-sampling the arXiv data set to a more balanced distribution. The findings show the confounding factors in the data cannot account for the inconsistency; instead, it should be due to the stochastic nature of the unsupervised algorithm. I also improve the approach by using ensemble topic models to derive representations. It is found the method to derive maps of science can be reproducible when it uses an ensemble topic model fused from a sufficient number of base models
Non-Compositional Term Dependence for Information Retrieval
Modelling term dependence in IR aims to identify co-occurring terms that are
too heavily dependent on each other to be treated as a bag of words, and to
adapt the indexing and ranking accordingly. Dependent terms are predominantly
identified using lexical frequency statistics, assuming that (a) if terms
co-occur often enough in some corpus, they are semantically dependent; (b) the
more often they co-occur, the more semantically dependent they are. This
assumption is not always correct: the frequency of co-occurring terms can be
separate from the strength of their semantic dependence. E.g. "red tape" might
be overall less frequent than "tape measure" in some corpus, but this does not
mean that "red"+"tape" are less dependent than "tape"+"measure". This is
especially the case for non-compositional phrases, i.e. phrases whose meaning
cannot be composed from the individual meanings of their terms (such as the
phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction
between the frequency and strength of term dependence in IR, we present a
principled approach for handling term dependence in queries, using both lexical
frequency and semantic evidence. We focus on non-compositional phrases,
extending a recent unsupervised model for their detection [21] to IR. Our
approach, integrated into ranking using Markov Random Fields [31], yields
effectiveness gains over competitive TREC baselines, showing that there is
still room for improvement in the very well-studied area of term dependence in
IR
Convolutional Neural Networks for Sentence Classification
We report on a series of experiments with convolutional neural networks (CNN)
trained on top of pre-trained word vectors for sentence-level classification
tasks. We show that a simple CNN with little hyperparameter tuning and static
vectors achieves excellent results on multiple benchmarks. Learning
task-specific vectors through fine-tuning offers further gains in performance.
We additionally propose a simple modification to the architecture to allow for
the use of both task-specific and static vectors. The CNN models discussed
herein improve upon the state of the art on 4 out of 7 tasks, which include
sentiment analysis and question classification.Comment: To appear in EMNLP 201
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