1,365 research outputs found
Towards the Automatic Classification of Documents in User-generated Classifications
There is a huge amount of information scattered on the World Wide Web. As the information flow occurs at a high speed in the WWW, there is a need to organize it in the right manner so that a user can access it very easily. Previously the organization of information was generally done manually, by matching the document contents to some pre-defined categories. There are two approaches for this text-based categorization: manual and automatic. In the manual approach, a human expert performs the classification task, and in the second case supervised classifiers are used to automatically classify resources. In a supervised classification, manual interaction is required to create some training data before the automatic classification task takes place. In our new approach, we intend to propose automatic classification of documents through semantic keywords and building the formulas generation by these keywords. Thus we can reduce this human participation by combining the knowledge of a given classification and the knowledge extracted from the data. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia, is the automatic classification of documents into user-generated classifications. The key benefits foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering, semantic index managing
An Entailment Relation for Reasoning on the Web
Reasoning on the Web is receiving an increasing attention because of emerging fields such as Web adaption and Semantic Web. Indeed, the advanced functionalities striven for in these fields call for reasoning capabilities. Reasoning on the Web, however, is usually done using existing techniques rarely fitting the Web. As a consequence, additional data processing like data conversion from Web formats (e.g. XML or HTML) into some other formats (e.g. classical logic terms and formulas) is often needed and aspects of the Web (e.g. its inherent inconsistency) are neglected. This article first gives requirements for an entailment tuned to reasoning on the Web. Then, it describes how classical logicās entailment can be modified so as to enforce these requirements. Finally, it discusses how the proposed entailment can be used in applying logic programming to reasoning on the Web
Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models
Vector space models of word meaning all share the assumption that words
occurring in similar contexts have similar meanings. In such models, words that
are similar in their topical associations but differ in their logical force
tend to emerge as semantically close, creating well-known challenges for NLP
applications that involve logical reasoning. Modern pretrained language models,
such as BERT, RoBERTa and GPT-3 hold the promise of performing better on
logical tasks than classic static word embeddings. However, reports are mixed
about their success. In the current paper, we advance this discussion through a
systematic study of scalar adverbs, an under-explored class of words with
strong logical force. Using three different tasks, involving both naturalistic
social media data and constructed examples, we investigate the extent to which
BERT, RoBERTa, GPT-2 and GPT-3 exhibit general, human-like, knowledge of these
common words. We ask: 1) Do the models distinguish amongst the three semantic
categories of MODALITY, FREQUENCY and DEGREE? 2) Do they have implicit
representations of full scales from maximally negative to maximally positive?
3) How do word frequency and contextual factors impact model performance? We
find that despite capturing some aspects of logical meaning, the models fall
far short of human performance.Comment: Published in BlackBoxNLP workshop, EMNLP 202
Generating multimedia presentations: from plain text to screenplay
In many Natural Language Generation (NLG) applications, the output is limited to plain text ā i.e., a string of words with punctuation and paragraph breaks, but no indications for layout, or pictures, or dialogue. In several projects, we have begun to explore NLG applications in which these extra media are brought into play. This paper gives an informal account of what we have learned. For coherence, we focus on the domain of patient information leaflets, and follow an example in which the same content is expressed first in plain text, then in formatted text, then in text with pictures, and finally in a dialogue script that can be performed by two animated agents. We show how the same meaning can be mapped to realisation patterns in different media, and how the expanded options for expressing meaning are related to the perceived style and tone of the presentation. Throughout, we stress that the extra media are not simple added to plain text, but integrated with it: thus the use of formatting, or pictures, or dialogue, may require radical rewording of the text itself
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
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Negation is a fundamental aspect of natural language, playing a critical role
in communication and comprehension. Our study assesses the negation detection
performance of Generative Pre-trained Transformer (GPT) models, specifically
GPT-2, GPT-3, GPT-3.5, and GPT-4. We focus on the identification of negation in
natural language using a zero-shot prediction approach applied to our custom
xNot360 dataset. Our approach examines sentence pairs labeled to indicate
whether the second sentence negates the first. Our findings expose a
considerable performance disparity among the GPT models, with GPT-4 surpassing
its counterparts and GPT-3.5 displaying a marked performance reduction. The
overall proficiency of the GPT models in negation detection remains relatively
modest, indicating that this task pushes the boundaries of their natural
language understanding capabilities. We not only highlight the constraints of
GPT models in handling negation but also emphasize the importance of logical
reliability in high-stakes domains such as healthcare, science, and law
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