1,720,996 research outputs found
Stylistic Experiments in Information Retrieval
No full textA discussion on various experiments to utilize stylistic variation among texts for information retrieval purposes
From Discourse to Logic: A Compositional Approach to Discourse Semantics
Get PDFIn this paper, we develop a system of rewriting rules, similar to the Generalized Phrase Structure Grammar and Montague Grammar, that operate directly on fragments of written text transforming it into well-formed expressions of a formal meaning representation language. We consider the task of translating a sentence into a formula of logic as being directly influenced by the context of the surrounding text. The resulting representation captures, besides the logical contents of each proposition, also the various relations in which they remain with respect to one another
An algorithm for inversing a unification parser into an efficient unification generator
No full textAbstractAn algorithm that inverses a unification parser written in PROLOG into a generator for natural language is presented. Some aspects of applicability of the algorithm are discussed
Loaded language and conspiracy theorizing
No full textMay 2023School of Humanities, Arts, and Social SciencesLoaded language is an umbrella term for words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others. Belief in conspiracy theories is tied to a range of strong emotions (van Prooijen and Douglas, 2018). Accordingly, language with strong emotional and persuasive content may be expressed by people experiencing the strong emotions associated with conspiracy theorizing. In this research, I examined multiple types of loaded language in three studies: (1) a comparison of loaded language on two online parenting forums for and against vaccination, (2) a comparison of loaded language on the subreddits r/conspiracy, r/science, and r/wallstreetbets, and (3) an evaluation of loaded language in a dataset from the alt-tech social networking platform Parler on January 6, 2021, when the U.S. Capitol was attacked as a consequence of conspiracy theories regarding the legitimacy of the 2020 Presidential Election. Results show that loaded language, whose usage is linked to the cognitive motivations underlying belief in conspiracy theories, is a linguistic marker of conspiracy theorizing.Ph
Fetilda : a framework for fin-tuned embeddings of long financial text documents
No full textMay 2022School of ScienceIncreasingly, unstructured data are being utilized in different domains. In particular, textual data, in recent years, is becoming more important. When it comes to financial applications, unstructured data, such as text from financial documents that companies publish on a consistent basis to government regulators like the Securities and Exchange Commission (SEC), is accumulating more and more. These financial documents are typically quite long, but they usually contain soft information that can be valuable in gauging company performance. They are special in that this soft information is not taken into consideration when trying to perform predictive analysis with only numerical data. Therefore, it would be very beneficial to train predictive models to learn on these long financial documents, in order to forecast metrics that gauge a company's future performance. And indeed, much progress has been made in the sphere of Natural Language Processing (NLP) in pre-trained language models (LMs) that train on huge corpora of texts. However, that progress is still lacking when it comes to effectively representing long documents. This is the focus of this thesis: we are looking at how to learn better models to utilize the beneficial information contained in long financial text documents and generate more informative features from text, in order to use the soft information for various regression tasks. Towards that end, we propose and implement a novel machine learning framework that divides a long document into chunks, inputs the chunks through different LMs, both pre-trained and from scratch, use the outputs from those chunks to generate chunk-level vector representations, and inputs that representation into a self-attention bi-LSTM network to generate a document-level representation. In order to evaluate our deep learning framework, we experiment on one dataset of 10-K financial reports published annually by banks in the US, and another dataset of 10-K reports published by publicly traded companies in the US. Our experiment results show that our approach outperforms strong baseline approaches in terms of textual modeling and a baseline regression approach utilizing only quantitative data. Our work shows that using pre-trained, domain-specific, and fine-tuned LMs in representing long texts betters the quality of the textual features generated, and improves the performance of prediction tasks.M
Towards a Generative Approach for Emotion Detection and Reasoning
No full textLarge language models (LLMs) have demonstrated impressive performance in mathematical and commonsense reasoning tasks using chain-of-thought (CoT) prompting techniques. But can they perform emotional reasoning by concatenating `Let's think step-by-step' to the input prompt? In this paper we investigate this question along with introducing a novel approach to zero-shot emotion detection and emotional reasoning using LLMs. Existing state of the art zero-shot approaches rely on textual entailment models to choose the most appropriate emotion label for an input text. We argue that this strongly restricts the model to a fixed set of labels which may not be suitable or sufficient for many applications where emotion analysis is required. Instead, we propose framing the problem of emotion analysis as a generative question-answering (QA) task. Our approach uses a two step methodology of generating relevant context or background knowledge to answer the emotion detection question step-by-step. Our paper is the first work on using a generative approach to jointly address the tasks of emotion detection and emotional reasoning for texts. We evaluate our approach on two popular emotion detection datasets and also release the fine-grained emotion labels and explanations for further training and fine-tuning of emotional reasoning systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An Evaluation of Two Grammar-Based Generation Algorithms: Essential Arguments Vs. Semantic-Head Driven Algorithm.
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