1,730 research outputs found
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Verb Physics: Relative Physical Knowledge of Actions and Objects
Learning commonsense knowledge from natural language text is nontrivial due
to reporting bias: people rarely state the obvious, e.g., "My house is bigger
than me." However, while rarely stated explicitly, this trivial everyday
knowledge does influence the way people talk about the world, which provides
indirect clues to reason about the world. For example, a statement like, "Tyler
entered his house" implies that his house is bigger than Tyler.
In this paper, we present an approach to infer relative physical knowledge of
actions and objects along five dimensions (e.g., size, weight, and strength)
from unstructured natural language text. We frame knowledge acquisition as
joint inference over two closely related problems: learning (1) relative
physical knowledge of object pairs and (2) physical implications of actions
when applied to those object pairs. Empirical results demonstrate that it is
possible to extract knowledge of actions and objects from language and that
joint inference over different types of knowledge improves performance.Comment: 11 pages, published in Proceedings of ACL 201
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
Comparative knowledge (e.g., steel is stronger and heavier than styrofoam) is
an essential component of our world knowledge, yet understudied in prior
literature. In this paper, we study the task of comparative knowledge
acquisition, motivated by the dramatic improvements in the capabilities of
extreme-scale language models like GPT-4, which have fueled efforts towards
harvesting their knowledge into knowledge bases. While acquisition of such
comparative knowledge is much easier from models like GPT-4, compared to their
considerably smaller and weaker counterparts such as GPT-2, not even the most
powerful models are exempt from making errors. We thus ask: to what extent are
models at different scales able to generate valid and diverse comparative
knowledge?
We introduce NeuroComparatives, a novel framework for comparative knowledge
distillation overgenerated from language models such as GPT-variants and Llama,
followed by stringent filtering of the generated knowledge. Our framework
acquires comparative knowledge between everyday objects, producing a corpus of
up to 8.8M comparisons over 1.74M entity pairs - 10X larger and 30% more
diverse than existing resources. Moreover, human evaluations show that
NeuroComparatives outperform existing resources (up to 32% absolute
improvement). We also demonstrate the utility of our distilled
NeuroComparatives on three downstream tasks. Our results show that
neuro-symbolic manipulation of smaller models offer complementary benefits to
the currently dominant practice of prompting extreme-scale language models for
knowledge distillation
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing for a high-coverage syntactic and
semantic text analysis combined with logical inference. For the syntactic and
semantic analysis we combine a deep semantic analysis with a shallow one
supported by statistical models in order to increase the quality and the
accuracy of results. For RTE we use logical inference of first-order employing
model-theoretic techniques and automated reasoning tools. The inference is
supported with problem-relevant background knowledge extracted automatically
and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or
other, more experimental sources with, e.g., manually defined presupposition
resolutions, or with axiomatized general and common sense knowledge. The
results show that fine-grained and consistent knowledge coming from diverse
sources is a necessary condition determining the correctness and traceability
of results.Comment: 25 pages, 10 figure
The Evaluation of Ontology Matching versus Text
Lately, the ontologies have become more and more complex, and they are used in different domains. Some of the ontologies are domain independent; some are specific to a domain. In the case of text processing and information retrieval, it is important to identify the corresponding ontology to a specific text. If the ontology is of a great scale, only a part of it may be reflected in the natural language text. This article presents metrics which evaluate the degree in which an ontology matches a natural language text, from word counting metrics to text entailment based metrics.Ontology, Natural Language Processing, Metric
A Discriminative Analysis of Fine-Grained Semantic Relations including Presupposition: Annotation and Classification
In contrast to classical lexical semantic relations between verbs, such as antonymy, synonymy or hypernymy, presupposition is a lexically triggered semantic relation that is not well covered in existing lexical resources. It is also understudied in the field of corpus-based methods of learning semantic relations. Yet, presupposition is very important for semantic and discourse analysis tasks, given the implicit information that it conveys. In this paper we present a corpus-based method for acquiring presupposition-triggering verbs along with verbal relata that express their presupposed meaning. We approach this difficult task using a discriminative classification method that jointly determines and distinguishes a broader set of inferential semantic relations between verbs.
The present paper focuses on important methodological aspects of our work: (i) a discriminative analysis of the semantic properties of the chosen set of relations, (ii) the selection of features for corpus-based classification and (iii) design decisions for the manual annotation of fine-grained semantic relations between verbs. (iv) We present the results of a practical annotation effort leading to a gold standard resource for our relation inventory, and (v) we report results for automatic classification of our target set of fine-grained semantic relations, including presupposition. We achieve a classification performance of 55% F1-score, a 100% improvement over a best-feature baseline
Medical WordNet: A new methodology for the construction and validation of information resources for consumer health
A consumer health information system must be able to comprehend both expert and non-expert medical vocabulary and to map between the two. We describe an ongoing
project to create a new lexical database called Medical WordNet (MWN), consisting of
medically relevant terms used by and intelligible to non-expert subjects and supplemented by a corpus of natural-language sentences that is designed to provide
medically validated contexts for MWN terms. The corpus derives primarily from online health information sources targeted to consumers, and involves two sub-corpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. The former consists of statements accredited as true on the basis of a rigorous process of validation, the latter of statements which non-experts believe to be true. We summarize the MWN / MFN / MBN project, and describe some of its applications
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