FrameAxis: Characterizing Microframe Bias and Intensity with Word
Embedding
- URL: http://arxiv.org/abs/2002.08608v4
- Date: Fri, 23 Jul 2021 01:45:20 GMT
- Title: FrameAxis: Characterizing Microframe Bias and Intensity with Word
Embedding
- Authors: Haewoon Kwak and Jisun An and Elise Jing and Yong-Yeol Ahn
- Abstract summary: We propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes ("microframes")
FrameAxis is designed to quantitatively tease out two important dimensions of how microframes are used in the text.
We demonstrate that microframes with the highest bias and intensity well align with sentiment, topic, and partisan spectrum by applying FrameAxis to multiple datasets from restaurant reviews to political news.
- Score: 8.278618225536807
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Framing is a process of emphasizing a certain aspect of an issue over the
others, nudging readers or listeners towards different positions on the issue
even without making a biased argument. {Here, we propose FrameAxis, a method
for characterizing documents by identifying the most relevant semantic axes
("microframes") that are overrepresented in the text using word embedding. Our
unsupervised approach can be readily applied to large datasets because it does
not require manual annotations. It can also provide nuanced insights by
considering a rich set of semantic axes. FrameAxis is designed to
quantitatively tease out two important dimensions of how microframes are used
in the text. \textit{Microframe bias} captures how biased the text is on a
certain microframe, and \textit{microframe intensity} shows how actively a
certain microframe is used. Together, they offer a detailed characterization of
the text. We demonstrate that microframes with the highest bias and intensity
well align with sentiment, topic, and partisan spectrum by applying FrameAxis
to multiple datasets from restaurant reviews to political news.} The existing
domain knowledge can be incorporated into FrameAxis {by using custom
microframes and by using FrameAxis as an iterative exploratory analysis
instrument.} Additionally, we propose methods for explaining the results of
FrameAxis at the level of individual words and documents. Our method may
accelerate scalable and sophisticated computational analyses of framing across
disciplines.
Related papers
- Contextual Document Embeddings [77.22328616983417]
We propose two complementary methods for contextualized document embeddings.
First, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss.
Second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.
arXiv Detail & Related papers (2024-10-03T14:33:34Z) - FrameFinder: Explorative Multi-Perspective Framing Extraction from News
Headlines [3.3181276611945263]
We present FrameFinder, an open tool for extracting and analyzing frames in textual data.
By analyzing the well-established gun violence frame corpus, we demonstrate the merits of our proposed solution.
arXiv Detail & Related papers (2023-12-14T14:41:37Z) - UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity [50.91030850662369]
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations.
This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios.
We contribute a new benchmark named textbfUFineBench for text-based person retrieval with ultra-fine granularity.
arXiv Detail & Related papers (2023-12-06T11:50:14Z) - Correspondence Matters for Video Referring Expression Comprehension [64.60046797561455]
Video Referring Expression (REC) aims to localize the referent objects described in the sentence to visual regions in the video frames.
Existing methods suffer from two problems: 1) inconsistent localization results across video frames; 2) confusion between the referent and contextual objects.
We propose a novel Dual Correspondence Network (dubbed as DCNet) which explicitly enhances the dense associations in both the inter-frame and cross-modal manners.
arXiv Detail & Related papers (2022-07-21T10:31:39Z) - TRIE++: Towards End-to-End Information Extraction from Visually Rich
Documents [51.744527199305445]
This paper proposes a unified end-to-end information extraction framework from visually rich documents.
Text reading and information extraction can reinforce each other via a well-designed multi-modal context block.
The framework can be trained in an end-to-end trainable manner, achieving global optimization.
arXiv Detail & Related papers (2022-07-14T08:52:07Z) - Fine-Grained Visual Entailment [51.66881737644983]
We propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image.
Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity.
We evaluate our method on a new dataset of manually annotated knowledge elements and show that our method achieves 68.18% accuracy at this challenging task.
arXiv Detail & Related papers (2022-03-29T16:09:38Z) - Representing Mixtures of Word Embeddings with Mixtures of Topic
Embeddings [46.324584649014284]
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions.
This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space.
Embedding the words and topics in the same vector space, we define a method to measure the semantic difference between the embedding vectors of the words of a document and these of the topics, and optimize the topic embeddings to minimize the expected difference over all documents.
arXiv Detail & Related papers (2022-03-03T08:46:23Z) - Sister Help: Data Augmentation for Frame-Semantic Role Labeling [9.62264668211579]
We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated.
We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation.
arXiv Detail & Related papers (2021-09-16T05:15:29Z) - Comprehensive Studies for Arbitrary-shape Scene Text Detection [78.50639779134944]
We propose a unified framework for the bottom-up based scene text detection methods.
Under the unified framework, we ensure the consistent settings for non-core modules.
With the comprehensive investigations and elaborate analyses, it reveals the advantages and disadvantages of previous models.
arXiv Detail & Related papers (2021-07-25T13:18:55Z) - Semantic Frame Induction using Masked Word Embeddings and Two-Step
Clustering [9.93359829907774]
We propose a semantic frame induction method using masked word embeddings and two-step clustering.
We demonstrate that using the masked word embeddings is effective for avoiding too much reliance on the surface information of frame-evoking verbs.
arXiv Detail & Related papers (2021-05-27T22:00:33Z) - Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents [17.672677325827454]
Images can give us insights into the contextual meanings of words, but current image-text grounding approaches require detailed annotations.
We present a simple unsupervised clustering-based method that increases precision and recall beyond object detection and image tagging baselines.
The proposed method is particularly effective for local contextual meanings of a word, for example associating "granite" with countertops in the real estate dataset and with rocky landscapes in a Wikipedia dataset.
arXiv Detail & Related papers (2020-10-30T16:39:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.