45 research outputs found
How Question Answering Technology Helps to Locate Malevolent Online Content
The inherent lack of control over the Internet content resulted in proliferation of online material that can be potentially detrimental. For example, the infamous “Anarchist Cookbook” teaching how to make weapons, home made bombs, and poisons, keeps re-appearing in various places. Some websites teach how to break into computer networks to steal passwords and credit card information. Law enforcement, security experts, and public watchdogs started to locate, monitor, and act when such malevolent content surfaces on the Internet. Since the resources of law enforcement are limited, it may take some time before potentially malevolent content is located, enough for it to disseminate and cause harm. Currently applied approach for searching the content of the Internet, available for law enforcement and public watchdogs is by using a search engine, such as Google, AOL, MSN, etc. We have suggested and empirically evaluated an alternative technology (called automated question answering or QA) capable of locating potentially malevolent online content. We have implemented a proof-of-concept prototype that is capable of finding web pages that may potentially contain the answers to specified questions (e.g. “How to steal a password?”). Using students as subjects in a controlled experiment, we have empirically established that our QA prototype finds web pages that are more likely to provide answers to given questions than simple keyword search using Google. This suggests that QA technology can be a good replacement or an addition to the traditional keyword searching for the task of locating malevolent online content and, possibly, for a more general task of interactive online information exploration
Advanced techniques for personalized, interactive question answering
Using a computer to answer questions has been a human dream since the beginning of
the digital era. A first step towards the achievement of such an ambitious goal is to deal
with naturallangilage to enable the computer to understand what its user asks.
The discipline that studies the conD:ection between natural language and the represen~
tation of its meaning via computational models is computational linguistics. According
to such discipline, Question Answering can be defined as the task that, given a question
formulated in natural language, aims at finding one or more concise answers in the form
of sentences or phrases.
Question Answering can be interpreted as a sub-discipline of information retrieval
with the added challenge of applying sophisticated techniques to identify the complex
syntactic and semantic relationships present in text. Although it is widely accepted that
Question Answering represents a step beyond standard infomiation retrieval, allowing a
more sophisticated and satisfactory response to the user's information needs, it still shares
a series of unsolved issues with the latter.
First, in most state-of-the-art Question Answering systems, the results are created
independently of the questioner's characteristics, goals and needs. This is a serious limitation
in several cases: for instance, a primary school child and a History student may
need different answers to the questlon: When did, the Middle Ages begin?
Moreover, users often issue queries not as standalone but in the context of a wider
information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces
would encourage and accommodate the submission of multiple related questions
and handle the user's requests for clarification, interactive Question Answering is still at
its early stages:
Furthermore, an i~sue which still remains open in current Question Answering is
that of efficiently answering complex questions, such as those invoking definitions and
descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess
the correctness of answers to such complex questions.
.. These are the central research problems addressed by this thesis, and are solved as
follows.
An in-depth study on complex Question Answering led to the development of classifiers
for complex answers. These exploit a variety of lexical, syntactic and shallow
semantic features to perform textual classification using tree-~ernel functions for Support
Vector Machines.
The issue of personalization is solved by the integration of a User Modelling corn':
ponent within the the Question Answering model. The User Model is able to filter and
fe-rank results based on the user's reading level and interests.
The issue ofinteractivity is approached by the development of a dialogue model and a
dialogue manager suitable for open-domain interactive Question Answering. The utility
of such model is corroborated by the integration of an interactive interface to allow reference
resolution and follow-up conversation into the core Question Answerin,g system and
by its evaluation.
Finally, the models of personalized and interactive Question Answering are integrated
in a comprehensive framework forming a unified model for future Question Answering
research
Enhancing factoid question answering using frame semantic-based approaches
FrameNet is used to enhance the performance of semantic QA systems. FrameNet is a linguistic resource that encapsulates Frame Semantics and provides scenario-based generalizations over lexical items that share similar semantic backgrounds.Doctor of Philosoph
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
Retrieving questions and answers in community-based question answering services
Ph.DDOCTOR OF PHILOSOPH