47 research outputs found
Token-Modification Adversarial Attacks for Natural Language Processing: A Survey
There are now many adversarial attacks for natural language processing
systems. Of these, a vast majority achieve success by modifying individual
document tokens, which we call here a \textit{token-modification} attack. Each
token-modification attack is defined by a specific combination of fundamental
\textit{components}, such as a constraint on the adversary or a particular
search algorithm. Motivated by this observation, we survey existing
token-modification attacks and extract the components of each. We use an
attack-independent framework to structure our survey which results in an
effective categorisation of the field and an easy comparison of components. We
hope this survey will guide new researchers to this field and spark further
research into the individual attack components.Comment: 8 pages, 1 figur
Paraphrastic language models
Natural languages are known for their expressive richness. Many sentences can be used to represent the same underlying meaning.
Only modelling the observed surface word sequence can result in poor context coverage and generalization, for example, when using
n-gram language models (LMs). This paper proposes a novel form of language model, the paraphrastic LM, that addresses these
issues. A phrase level paraphrase model statistically learned from standard text data with no semantic annotation is used to generate
multiple paraphrase variants. LM probabilities are then estimated by maximizing their marginal probability. Multi-level language
models estimated at both the word level and the phrase level are combined. An efficient weighted finite state transducer (WFST)
based paraphrase generation approach is also presented. Significant error rate reductions of 0.5–0.6% absolute were obtained over the
baseline n-gram LMs on two state-of-the-art recognition tasks for English conversational telephone speech and Mandarin Chinese
broadcast speech using a paraphrastic multi-level LM modelling both word and phrase sequences. When it is further combined with
word and phrase level feed-forward neural network LMs, a significant error rate reduction of 0.9% absolute (9% relative) and 0.5%
absolute (5% relative) were obtained over the baseline n-gram and neural network LMs respectivelyThe research leading to these results was supported by EPSRC grant EP/I031022/1 (Natural Speech Technology)
and DARPA under the Broad Operational Language Translation (BOLT) program.This version is the author accepted manuscript. The final published version can be found on the publisher's website at:http://www.sciencedirect.com/science/article/pii/S088523081400028X# © 2014 Elsevier Ltd. All rights reserved
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
Word representation has always been an important research area in the history
of natural language processing (NLP). Understanding such complex text data is
imperative, given that it is rich in information and can be used widely across
various applications. In this survey, we explore different word representation
models and its power of expression, from the classical to modern-day
state-of-the-art word representation language models (LMS). We describe a
variety of text representation methods, and model designs have blossomed in the
context of NLP, including SOTA LMs. These models can transform large volumes of
text into effective vector representations capturing the same semantic
information. Further, such representations can be utilized by various machine
learning (ML) algorithms for a variety of NLP related tasks. In the end, this
survey briefly discusses the commonly used ML and DL based classifiers,
evaluation metrics and the applications of these word embeddings in different
NLP tasks
A Review of Text Corpus-Based Tourism Big Data Mining
With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years