Papers with text analysis
Wikipedia as a Resource for Text Analysis and Retrieval (P19-4)
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| Challenge: | Tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. |
| Approach: | tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. |
| Outcome: | This tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. |
YATO: Yet Another deep learning based Text analysis Open toolkit (2023.emnlp-demo)
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| Challenge: | YATO is an open-source toolkit for text analysis with deep learning . it supports free combinations of three types of widely used features . |
| Approach: | They introduce YATO, an open-source toolkit for text analysis with deep learning. |
| Outcome: | YATO is an open-source toolkit for text analysis with deep learning . the toolkit supports free combinations of three types of widely used features . |
Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy (2021.findings-emnlp)
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| Challenge: | Latent Dirichlet allocation (LDA) is a widely used topic model to discover the latent semantic of text data. |
| Approach: | They propose to combine a subsampling method with CGS to improve efficiency while amplifying privacy by using a novel metric, the efficiency–privacy function. |
| Outcome: | The proposed algorithm improves efficiency while amplifying privacy while subsampling in CGS increases efficiency while preserving privacy. |
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages (2020.acl-demos)
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| Challenge: | Existing tools that support only a few major languages are under-optimized for accuracy due to a focus on efficiency or use of less powerful models. |
| Approach: | They introduce a Python natural language processing toolkit that supports 66 languages . they train Stanza on 112 datasets and show it generalizes well on all languages compared to other tools . |
| Outcome: | The proposed toolkit performs well on 112 datasets and is compatible with the popular Java CoreNLP software. |
PUCP-Metrix: An Open-source and Comprehensive Toolkit for Linguistic Analysis of Spanish Texts (2026.eacl-demo)
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| Challenge: | Existing tools for linguistic analysis of Spanish texts lack linguistic features for interpretability and tasks that involve style, structure, and readability. |
| Approach: | They propose to use PUCP-Metrix to analyze Spanish texts in a language repository. |
| Outcome: | The proposed toolkit performs better on automated readability assessments and machine-generated text detection tasks than existing repositories and strong neural baselines. |
Unlocking the Heterogeneous Landscape of Big Data NLP with DUUI (2023.findings-emnlp)
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| Challenge: | Automated analysis of large corpora is a complex task, especially in terms of time efficiency. |
| Approach: | They propose a framework for automatic distributed analysis of text corpora that leverages Big Data experience and virtualization with Docker. |
| Outcome: | The proposed framework is scalable, flexible, lightweight, and feature-rich for automatic distributed analysis of text corpora. |
On Fake News Detection with LLM Enhanced Semantics Mining (2024.emnlp-main)
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| Challenge: | Existing methods for detecting fake news use only news embeddings to capture the lexical semantics between tokens. |
| Approach: | They propose a topic-based model with prompts to extract news embeddings from LLMs and a generalized page-rank model to extract local and global semantics. |
| Outcome: | The proposed model shows superior performance on five benchmark datasets over seven baseline methods. |
Heterogeneous Supervised Topic Models (2022.tacl-1)
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| Challenge: | Researchers in the social sciences are interested in the relationship between text and an outcome of interest. |
| Approach: | They develop a probabilistic approach to text analysis and prediction using a joint model of text and outcomes to find heterogeneous patterns. |
| Outcome: | The proposed model outperforms other methods on eight datasets and consistently outperformed other models. |
What’s Wrong with Hebrew NLP? And How to Make it Right (D19-3)
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| Challenge: | Sub-optimal performance of many morphologically rich languages (MRLs) is due to errors in early morphology disambiguation decisions, that cannot be recovered later on in the pipeline, yielding incoherent annotations on the whole. |
| Approach: | They propose to use a joint morpho-syntactic infrastructure for processing Modern Hebrew texts to provide rich and expressive annotations. |
| Outcome: | The proposed pipelines are based on a morpho-syntactic infrastructure for processing Modern Hebrew texts. |
Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis (P18-1)
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| Challenge: | Existing approaches to text analysis make no assumptions about linguistic structure and focus on stastically frequent patterns. |
| Approach: | They propose a new strategy to visualize linguistic information detected by a CNN for text classification. |
| Outcome: | The proposed strategy automatically encodes complex linguistic patterns on three different languages for each dataset. |
MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline (2024.findings-acl)
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| Challenge: | Large language models lack mathematical reasoning, a hurdle on the path to true artificial general intelligence. |
| Approach: | They propose a protocol for fine-tuning large language models with a Python code interpreter to enhance the text analysis of the LLMs. |
| Outcome: | The proposed protocol improves the performance of a 7B-parameter LLM on the GSM8K and MATH datasets while allowing for an outlier-free value model-based inference method. |
Interactive Construction of User-Centric Dictionary for Text Analytics (2020.acl-main)
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| Challenge: | Existing methods for interactive dictionary construction are limited to a small number of terms, but we propose a method that can be used to create flexible dictionaries with precise granularity. |
| Approach: | They propose a method to construct a term dictionary for text analytics through an interactive process between a human and a machine. |
| Outcome: | The proposed method outperforms baseline methods and works even with a small number of interactions. |
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions (2020.coling-main)
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| Challenge: | Existing methods for estimating event salience without annotations are prohibitively costly because they require annotators to understand the concept of event salientity. |
| Approach: | They propose to use Barthes’ definition of event salience to compute event salientity without annotations by using a pre-trained language model. |
| Outcome: | The proposed methods outperform baseline methods on folktales with event salience annotation and fine-tuned language model is key factor in improving the methods. |
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction (2024.findings-acl)
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| Challenge: | Recent work has focused on the performance of Large Language Models (LLMs) but the finance sector is relying on time-series data for complex forecasting tasks. |
| Approach: | They propose a framework that employs Sequential Knowledge-Guided Prompting to identify factors that influence stock movements using LLMs. |
| Outcome: | The proposed framework outperforms existing methods and is effective in time-series forecasting. |
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection (2023.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable zero-shot performance across various NLP tasks. |
| Approach: | They propose a method which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner and prompts an LLM to rate individual items at each turn. |
| Outcome: | The proposed method improves the performance and robustness of the standard GPT-3.5 personality detection task on two benchmark datasets. |
Fusion of Detected Objects in Text for Visual Question Answering (D19-1)
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| Challenge: | Recent neural architectures such as Transformer and BERT allow for multimodal context, which may help model the meaning of words in general and also sharpen its understanding of instances of words. |
| Approach: | They propose a neural architecture that combines vision and natural language to advance models of multimodal context. |
| Outcome: | The proposed architecture achieves the highest performance on the Visual Commonsense Reasoning benchmark and the best performance to date on the public leaderboard. |
Towards Entity Spaces (2020.lrec-1)
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| Challenge: | Entities are a central element of knowledge bases and are used in many knowledge-centric tasks including text analysis. |
| Approach: | They propose to use entity spaces to represent a set of associated entities with near-identity to provide a handle to an amorphous grouping of entities. |
| Outcome: | The proposed representations improve recall of entity linking in English by using disambiguation pages. |
Large Language Models for Predictive Analysis: How Far Are They? (2025.findings-acl)
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| Challenge: | Existing studies on LLMs do not evaluate their capability in predictive analysis. |
| Approach: | They propose a benchmark to evaluate Large Language Models (LLMs) they integrate 1130 queries from 44 real-world datasets of 8 different fields to evaluate their capability . |
| Outcome: | The proposed benchmark evaluates 12 renowned LLMs from 44 real-world datasets . results offer insights into their practical use in predictive analysis . |
Auto-Encoding Variational Bayes for Inferring Topics and Visualization (2020.coling-main)
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| Challenge: | Visualization and topic modeling are widely used for text analysis, but there is a lack of semantic interpretation as they do not extract topics. |
| Approach: | They propose a fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization using a single generative model. |
| Outcome: | The proposed method can handle model changes efficiently with little rederivation effort. |
WikiDragon: A Java Framework For Diachronic Content And Network Analysis Of MediaWikis (L18-1)
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| Challenge: | WikiDragon is a Java Framework designed to give developers in computational linguistics an intuitive API to build, parse and analyze instances of MediaWikis. |
| Approach: | They introduce WikiDragon, a Java Framework that allows developers to build, parse and analyze instances of MediaWikis on their computers. |
| Outcome: | The framework is based on the Wikipedia, Wiktionary, WikiSource or WikiNews and evaluates link extraction, diachronic network analysis and the impact of different frameworks to text analysis. |
CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform (2022.lrec-1)
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| Challenge: | Existing tools to analyze linguistic complexity are limited and different because of different research purposes. |
| Approach: | They propose to integrate Chinese component into CTAP to analyze linguistic complexity . they propose to use 196 linguistic complex indexes to calculate linguistic characteristics . |
| Outcome: | The proposed indexes are compared with three linguistic complexity tools for Chinese . the proposed index sets include four levels of 196 linguistic complex indexe . |
End-to-end Aspect-based Sentiment Analysis with Combinatory Categorial Grammar (2023.findings-acl)
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| Challenge: | End-to-end aspect-based sentiment analysis (EASA) is a natural language processing task that requires a deep understanding of the running text. |
| Approach: | They propose a method to improve EASA with CCG supertags that carry syntactic and semantic information of the associated words. |
| Outcome: | The proposed approach outperforms baselines and achieves state-of-the-art results on all datasets. |
Harnessing Large Language Models as Post-hoc Correctors (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in a wide range of tasks, including machine translation and commonsense reasoning. |
| Approach: | They propose a training-free framework that can work as a post-hoc corrector to propose corrections for ML models. |
| Outcome: | The proposed framework improves the performance of a number of models by up to 39% on text analysis and the challenging molecular predictions. |
Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling (2025.findings-emnlp)
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| Challenge: | Existing methods for topic modeling fail to scale to large datasets or assume one topic per document. |
| Approach: | They propose a topic modeling technique that discovers multiple topics per sample . they evaluate SCA on Twitter datasets in English, Hausa and Chinese . |
| Outcome: | The proposed technique outperforms the LLM-based TopicGPT on Twitter datasets with similar compute budgets. |
From Complexity to Clarity: AI/NLP’s Role in Regulatory Compliance (2025.findings-acl)
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| Challenge: | Recent advances in natural language processing have demonstrated remarkable capabilities in text analysis and reasoning. |
| Approach: | They propose to use standardized evaluation frameworks and balanced human-AI collaboration to address these challenges. |
| Outcome: | The proposed research will focus on standardized evaluation frameworks and balanced human-AI collaboration to address these challenges. |
Towards Building the LEMI Readability Platform for Children’s Literature in the Romanian Language (2024.lrec-main)
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Madalina Chitez, Mihai Dascalu, Aura Cristina Udrea, Cosmin Strilețchi, Karla Csürös, Roxana Rogobete, Alexandru Oravițan
| Challenge: | Currently, no existing platform integrates a research-based readability formula for the Romanian language, making this tool unique. |
| Approach: | They propose a new readability tool for children’s literature in the Romanian language that uses a self-compiled corpus and a text analysis interface to generate automatic readability reports for uploaded short texts. |
| Outcome: | The proposed readability tool is specifically targeted at primary school students aged 7-11 . it extracts, tests, and calibrates a readability formula for Romanian using the children’s literature corpus and the platform functionalities. |