Papers with machine learning techniques
Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
| Approach: | They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text. |
| Outcome: | The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences. |
Automatic Construction of Enterprise Knowledge Base (2021.emnlp-demo)
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| Challenge: | Existing knowledge bases are often based on bootstrapping entities from human-curated sources such as Wikipedia. |
| Approach: | They propose to build a knowledge base from enterprise documents with minimal human intervention by using deep learning models and classical machine learning techniques. |
| Outcome: | The proposed system is currently serving as part of a Microsoft 365 service. |
Discreteness in Neural Natural Language Processing (D19-2)
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| Challenge: | This tutorial provides a comprehensive guide to the process of discreteness in neural NLP. |
| Approach: | This tutorial provides a comprehensive guide to the process of discreteness in neural NLP. |
| Outcome: | This tutorial explains the process of discreteness in neural NLP. |
Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort (C18-1)
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| Challenge: | Currently, few or no language processing tools or resources exist for most languages . a problem is that there is not enough available training data even in resource-rich languages if the task is complex. |
| Approach: | They propose to use a bilingual dictionary to train machine learning in a resource-poor language . they also explore adversarial training of bilingual word representations . |
| Outcome: | The proposed approach gives similar performance in event-type detection tasks. |
Complaint Analysis and Classification for Economic and Food Safety (D19-51)
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João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, Ana Maria Oliveira
| Challenge: | Governmental institutions are using artificial intelligence to deal with specific problems and exploit their huge amounts of structured and unstructured information. |
| Approach: | They propose to use natural language processing and machine learning to classify complaints . they use feature-based approaches and traditional classifiers to analyze complaints based on citizen feedback . |
| Outcome: | The proposed methods have accuracy scores above 70% and can be used to improve public services. |
Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)
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| Challenge: | Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news. |
| Approach: | This paper presents a comprehensive list of datasets used for rumor detection . it also reviews the important studies based on what types of information they exploit . |
| Outcome: | This paper presents an overview of the recent studies in the rumor detection field . it provides a comprehensive list of datasets used for rumour detection . |
Situation-Based Multiparticipant Chat Summarization: a Concept, an Exploration-Annotation Tool and an Example Collection (2021.acl-srw)
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| Challenge: | Currently, text chat does not offer navigation or full-featured search, although the high volumes of messages demand it. |
| Approach: | They propose a data annotation tool for situation-based summarization that can be used to extract messages from chat logs. |
| Outcome: | The proposed tool is the first to be developed for situation-based summarization. |
Generating Continuous Representations of Medical Texts (N18-5)
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| Challenge: | Obtaining a good representation for medical texts may prove vital to building more sophisticated generative, discriminative or semantic models for the field. |
| Approach: | They propose an autoencoder that generates medical texts while learning an informative, continuous representation with discriminative features. |
| Outcome: | The proposed system generates medical texts while learning an informative, continuous representation with discriminative features. |
Ukrainian Resilience: A Dataset for Detection of Help-Seeking Signals Amidst the Chaos of War (2024.findings-emnlp)
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| Challenge: | a new dataset is being developed to categorize posts that show distress or urgency . the dataset could improve humanitarian efforts, allowing for quicker and more targeted help . |
| Approach: | They propose a dataset that brings together social media posts in the Ukrainian language for the detection of help-seeking posts in times of war. |
| Outcome: | The proposed dataset can be used to improve humanitarian efforts . it can be compared with existing datasets and achieve an accuracy of 81.15% . |
Corpus Creation and Emotion Prediction for Hindi-English Code-Mixed Social Media Text (N18-4)
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| Challenge: | Emotion Prediction is a natural language processing task dealing with detection and classification of emotions in monolingual and bilingual texts. |
| Approach: | They propose a machine learning system which uses various machine learning techniques to detect emotion associated with tweets. |
| Outcome: | The proposed system uses various machine learning techniques to detect emotion associated with the text. |
Litigation Analytics: Extracting and querying motions and orders from US federal courts (N19-4)
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| Challenge: | a detailed manual analysis of a docket could provide valuable information for the suit and the respective judge. |
| Approach: | They applied machine learning and machine learning to extract and aggregate docket statistics . they used a search engine to query the data in real time and a question-answering interface . |
| Outcome: | The proposed method extracts information from 8 million federal dockets and keeps up with newly closed docketes. |
Automated Scoring: Beyond Natural Language Processing (C18-1)
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| Challenge: | In this paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond competitive shared tasks. |
| Approach: | They argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks . they argue that it is essential for us as NLP researchers to understand and incorporate these perspectives in our research and work towards a mutually satisfactory solution . |
| Outcome: | The proposed approach is based on the findings of a recent conference on automated scoring. |
Candidate Ranking for Maintenance of an Online Dictionary (L18-1)
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| Challenge: | lexicographers have traditionally identified a lexical item to add to a dictionary . but in the modern age of online dictionaries, queries for lexicals are indistinguishable from a larger list of misspellings . a system that uses machine learning techniques to assign "misspells" a probability of being a novel or missing entry is developed . |
| Approach: | They develop a system that uses machine learning techniques to assign "misspells" a probability of being a novel or missing entry. |
| Outcome: | The proposed system assigns "misspells" a probability of being a novel or missing entry . it uses signals from orthography, usage by trusted online sources, and dictionary query patterns . |
Social Image Tags as a Source of Word Embeddings: A Task-oriented Evaluation (L18-1)
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| Challenge: | Distributional hypothesis-based word representations lack perceptual and empirical knowledge. |
| Approach: | They evaluate the effectiveness of social image tags in generating word embeddings . they find that generated word embeds exhibit somewhat different behaviors from corpus-originated representations - authors . |
| Outcome: | The generated word embeddings exhibit comparable performance with corpus-originated representations. |
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (2024.lrec-main)
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| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
| Approach: | They propose a method that uses a deep dilated residual convolution encoder to learn document representations across different lengths of the texts. |
| Outcome: | The proposed method achieves state-of-the-art performance on a number of measures. |
Abusive language in Spanish children and young teenager’s conversations: data preparation and short text classification with contextual word embeddings (2020.lrec-1)
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| Challenge: | Existing studies on how to automatically detect abusive short texts are gaining interest in the natural language processing community. |
| Approach: | They propose to use a contextual word embedding model to automatically detect abusive short texts for Spanish language. |
| Outcome: | The proposed model outperforms classical methods in the detection of abusive short texts for the spanish language. |
Method Entity Extraction from Biomedical Texts (2022.coling-1)
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| Challenge: | Scientific research papers consist of complex keywords and domain-specific terminologies, and new terminologie erupt. |
| Approach: | They find method terminologies in biomedical text using rule-based and machine learning techniques . authors propose to use a silver standard corpus to extract method entities from biomedically text . |
| Outcome: | The proposed method entities can be extracted from biomedical text with reasonable accuracy . the proposed method entity extraction method is based on a rule-based method and a machine learning technique. |
Benchmarking Intersectional Biases in NLP (2022.naacl-main)
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| Challenge: | Recent work on fairness of machine learning models has focused on how to debias, but research on the fairness and performance of biased/debiased models on downstream prediction tasks has been limited. |
| Approach: | They assess intersectional bias - fairness across multiple demographic dimensions . they highlight possible causes and make recommendations for future NLP debiasing research. |
| Outcome: | The proposed approaches fare well in terms of fairness-accuracy trade-off, but are unable to effectively alleviate bias in downstream tasks. |
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization (2020.acl-main)
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| Challenge: | Recent studies show that data-driven machine learning models carry societal biases in the dataset they trained on. |
| Approach: | They propose to calibrate top predictions of a model by injecting corpus-level constraints to ensure that the gender disparity is not amplified. |
| Outcome: | The proposed method can almost remove bias amplification in the distribution with little loss of performance. |
Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages (2022.lrec-1)
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| Challenge: | voicing alternation phenomena of stops are a common problem in connected speech . phoneticians and phonologists are interested in analyzing phonetic variation . |
| Approach: | They use forced alignment with pronunciation variants and machine learning techniques to examine voicing alternations of stops in Romance languages. |
| Outcome: | The proposed method enables linguists to use large corpora and speech recognition systems . the results show that voicing alternations occur in all Romance languages . |
CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation (2023.acl-long)
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| Challenge: | Automated diagnosis (AD) is a critical application of AI in healthcare . despite its simplicity and superior performance, a decline in disease diagnosis accuracy is observed . |
| Approach: | They propose a new collaborative disease and symptom generation framework to improve automatic diagnosis. |
| Outcome: | The Transformer-based method achieves an average 2.3% improvement over previous state-of-the-art methods . it can be used to query patients about their symptoms and health concerns . |
WordKit: a Python Package for Orthographic and Phonological Featurization (L18-1)
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| Challenge: | wordkit is a python package that allows users to switch between feature sets and featurizers with a uniform API . wordkit integrates orthographic and phonological featurizers in a single package . |
| Approach: | They present a python package which allows users to switch between feature sets and featurizers with a uniform API. |
| Outcome: | The proposed package is compatible with scikit-learn and extensible . it allows users to switch between feature sets and featurizers with a uniform API . |
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text (P19-1)
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| Challenge: | Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the best combination of them. |
| Approach: | They propose a novel AutoML strategy based on probabilistic grammatical evolution to explore the best combination of parameters and features to use when dealing with the knowledge discovery challenge in Spanish text documents. |
| Outcome: | The proposed strategy achieves state-of-the-art and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. |
Annotation and Analysis of Extractive Summaries for the Kyutech Corpus (L18-1)
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| Challenge: | Summarization of multi-party conversation requires corpora to analyze characteristics of conversations and construct a method for summary generation. |
| Approach: | They propose to annotate a Japanese conversation corpus for a decision-making task . they compare extractive summarization methods with the annotated extractive summary . |
| Outcome: | The proposed corpus is the first annotated for conversation summarization tasks and freely available to anyone. |
Discovering the Language of Wine Reviews: A Text Mining Account (L18-1)
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| Challenge: | odors and flavors are often expressed in wine reviews, but they are often not. |
| Approach: | They use a corpus of wine reviews to find out what wine is like in a review . they use lexical bag-of-words features, domain-specific terminology features and word embedding features to train machine learning. |
| Outcome: | The proposed model predicts the wine's color, grape variety, and country of origin based on the review text alone. |
BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts (2022.lrec-1)
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Nauros Romim, Mosahed Ahmed, Md Saiful Islam, Arnab Sen Sharma, Hriteshwar Talukder, Mohammad Ruhul Amin
| Challenge: | Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS) due to the massive amount of user-generated content, modern machine learning techniques are feasible and cost-effective to tackle this problem. |
| Approach: | They propose to use a large manually labeled Bangla HS dataset to train generalizable models. |
| Outcome: | The proposed dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than existing Bangla HS datasets. |
Measure Children’s Mindreading Ability with Machine Reading (2023.findings-emnlp)
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| Challenge: | Existing scoring models do not take the features of the stories and video clips into account when scoring, which will reduce the accuracy of the models. |
| Approach: | They propose to leverage the features extracted from stories and videos related to the questions being asked during the children’s mindreading evaluation. |
| Outcome: | The proposed framework agrees well with human experts on scores produced by the models. |
Ontology Matching Using Convolutional Neural Networks (2020.lrec-1)
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| Challenge: | a growing number of ontologies require effective ways to align different ontology models . traditional methods to align ontological models are based on string metrics and structure analysis . but convolutional neural networks can be applied as-is to any domain, allowing for cross-domain applications . |
| Approach: | They propose a method to align ontologies automatically using machine learning techniques . they use convolutional neural networks to perform string matching between class labels . |
| Outcome: | The proposed method achieves state-of-the-art on ontologies from the Ontology Alignment Evaluation Initiative (OAEI) it maintains good performance when tested on a different domain, which could lead to cross-domain applications. |
Identifying Fine-grained Depression Signs in Social Media Posts (2024.lrec-main)
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| Challenge: | Currently, most studies focus on a binary classification setup or on pre-established resources. |
| Approach: | They evaluated machine learning techniques to model 21 depression signs in social media posts from Brazilian undergraduate students. |
| Outcome: | The proposed methods struggle to classify the majority of depression signs on social media posts, compared with the majority on the social media sites. |
Advancing Vision-Language Models with Adapter Ensemble Strategies (2024.findings-emnlp)
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| Challenge: | CLIP revolutes vision-language pretraining by using contrastive learning on paired web data. |
| Approach: | They propose to combine a "adapter ensemble" with traditional machine learning techniques to augment large-scale pretrained vision-language models. |
| Outcome: | The proposed model outperforms baselines and derives improvement when the number of ensemble parameters increases. |
Assessing French Readability for Adults with Low Literacy: A Global and Local Perspective (2025.emnlp-main)
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| Challenge: | illiterate individuals are persons aged 15 years and above who cannot read and write with understanding a short simple statement on their everyday life. |
| Approach: | They propose a novel approach to assess french text readability for adults with low literacy skills using a global and segment-level difficulty scale. |
| Outcome: | The proposed approach addresses both global (full-text) and local (segment-level) difficulty scales. |
Predictive and Distinctive Linguistic Features in Schizophrenia-Bipolar Spectrum Disorders (2024.lrec-main)
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| Challenge: | Using this data, we analyze different linguistic features’ predictive power by computing and comparing their frequency distributions. |
| Approach: | They analyze speech transcripts from Hungarian patients with schizophrenia, schizoaffective, and bipolar disorders and compare their linguistic features to identify distinctive linguistic characteristics. |
| Outcome: | The proposed method outperforms baseline methods in distinguishing between schizophrenia, schizoaffective, and bipolar disorders. |