2,066 research outputs found
Requirements Classification with Interpretable Machine Learning and Dependency Parsing
Requirements classification is a traditional application of machine learning (ML) to RE that helps handle large requirements datasets. A prime example of an RE classification problem is the distinction between functional and non-functional (quality) requirements. State-of-the-art classifiers build their effectiveness on a large set of word features like text n-grams or POS n-grams, which do not fully capture the essence of a requirement. As a result, it is arduous for human analysts to interpret the classification results by exploring the classifier's inner workings. We propose the use of more general linguistic features, such as dependency types, for the construction of interpretable ML classifiers for RE. Through a feature engineering effort, in which we are assisted by modern introspection tools that reveal the hidden inner workings of ML classifiers, we derive a set of 17 linguistic features. While classifiers that use our proposed features fit the training set slightly worse than those that use high-dimensional feature sets, our approach performs generally better on validation datasets and it is more interpretable
Explainable Misinformation Detection Across Multiple Social Media Platforms
In this work, the integration of two machine learning approaches, namely
domain adaptation and explainable AI, is proposed to address these two issues
of generalized detection and explainability. Firstly the Domain Adversarial
Neural Network (DANN) develops a generalized misinformation detector across
multiple social media platforms DANN is employed to generate the classification
results for test domains with relevant but unseen data. The DANN-based model, a
traditional black-box model, cannot justify its outcome, i.e., the labels for
the target domain. Hence a Local Interpretable Model-Agnostic Explanations
(LIME) explainable AI model is applied to explain the outcome of the DANN mode.
To demonstrate these two approaches and their integration for effective
explainable generalized detection, COVID-19 misinformation is considered a case
study. We experimented with two datasets, namely CoAID and MiSoVac, and
compared results with and without DANN implementation. DANN significantly
improves the accuracy measure F1 classification score and increases the
accuracy and AUC performance. The results obtained show that the proposed
framework performs well in the case of domain shift and can learn
domain-invariant features while explaining the target labels with LIME
implementation enabling trustworthy information processing and extraction to
combat misinformation effectively.Comment: 28 pages,4 figure
Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning
Personalized recommendations have a growing importance in direct marketing,
which motivates research to enhance customer experiences by knowledge graph
(KG) applications. For example, in financial services, companies may benefit
from providing relevant financial articles to their customers to cultivate
relationships, foster client engagement and promote informed financial
decisions. While several approaches center on KG-based recommender systems for
improved content, in this study we focus on interpretable KG-based recommender
systems for decision making.To this end, we present two knowledge graph-based
approaches for personalized article recommendations for a set of customers of a
large multinational financial services company. The first approach employs
Reinforcement Learning and the second approach uses the XGBoost algorithm for
recommending articles to the customers. Both approaches make use of a KG
generated from both structured (tabular data) and unstructured data (a large
body of text data).Using the Reinforcement Learning-based recommender system we
could leverage the graph traversal path leading to the recommendation as a way
to generate interpretations (Path Directed Reasoning (PDR)). In the
XGBoost-based approach, one can also provide explainable results using post-hoc
methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I
am Five).Importantly, our approach offers explainable results, promoting better
decision-making. This study underscores the potential of combining advanced
machine learning techniques with KG-driven insights to bolster experience in
customer relationship management.Comment: Accepted at KDD (OARS) 2023 [https://oars-workshop.github.io/
Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit
The primary focus of this thesis is to make Sanskrit manuscripts more
accessible to the end-users through natural language technologies. The
morphological richness, compounding, free word orderliness, and low-resource
nature of Sanskrit pose significant challenges for developing deep learning
solutions. We identify four fundamental tasks, which are crucial for developing
a robust NLP technology for Sanskrit: word segmentation, dependency parsing,
compound type identification, and poetry analysis. The first task, Sanskrit
Word Segmentation (SWS), is a fundamental text processing task for any other
downstream applications. However, it is challenging due to the sandhi
phenomenon that modifies characters at word boundaries. Similarly, the existing
dependency parsing approaches struggle with morphologically rich and
low-resource languages like Sanskrit. Compound type identification is also
challenging for Sanskrit due to the context-sensitive semantic relation between
components. All these challenges result in sub-optimal performance in NLP
applications like question answering and machine translation. Finally, Sanskrit
poetry has not been extensively studied in computational linguistics.
While addressing these challenges, this thesis makes various contributions:
(1) The thesis proposes linguistically-informed neural architectures for these
tasks. (2) We showcase the interpretability and multilingual extension of the
proposed systems. (3) Our proposed systems report state-of-the-art performance.
(4) Finally, we present a neural toolkit named SanskritShala, a web-based
application that provides real-time analysis of input for various NLP tasks.
Overall, this thesis contributes to making Sanskrit manuscripts more accessible
by developing robust NLP technology and releasing various resources, datasets,
and web-based toolkit.Comment: Ph.D. dissertatio
D4.1. Technologies and tools for corpus creation, normalization and annotation
The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition
A Systematic Survey on Deep Generative Models for Graph Generation
Graphs are important data representations for describing objects and their
relationships, which appear in a wide diversity of real-world scenarios. As one
of a critical problem in this area, graph generation considers learning the
distributions of given graphs and generating more novel graphs. Owing to its
wide range of applications, generative models for graphs have a rich history,
which, however, are traditionally hand-crafted and only capable of modeling a
few statistical properties of graphs. Recent advances in deep generative models
for graph generation is an important step towards improving the fidelity of
generated graphs and paves the way for new kinds of applications. This article
provides an extensive overview of the literature in the field of deep
generative models for the graph generation. Firstly, the formal definition of
deep generative models for the graph generation as well as preliminary
knowledge is provided. Secondly, two taxonomies of deep generative models for
unconditional, and conditional graph generation respectively are proposed; the
existing works of each are compared and analyzed. After that, an overview of
the evaluation metrics in this specific domain is provided. Finally, the
applications that deep graph generation enables are summarized and five
promising future research directions are highlighted
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