MOTIVATION: Accurate TNM staging from lung cancer radiology reports is crucial for treatment planning and prognosis assessment. Manual staging processes are time-consuming and subject to inter-observer variability. Large language models (LLMs) offer opportunities to automate TNM staging with enhanced interpretability and clinical reasoning. RESULTS: We developed two complementary systems for automated TNM staging from English radiology reports. System I employs GPT-4o with reasoning-based few-shot learning and multi-step voting. System II integrates multiple LLMs (GPT-4o and Gemini-2) using DSPy framework with MIPROv2 optimization. In NTCIR-18 RadNLP 2024 English main task, our approaches achieved first (joint accuracy: 0.6543) and second place (joint accuracy: 0.6296), demonstrating superior performance in T, N, and M classification with accuracies of 0.7037/0.9136/0.8889 and 0.7284/0.9383/0.8395, respectively. AVAILABILITY AND IMPLEMENTATION: Source code freely available at https://github.com/nlptmu/multi-expert-tnm-staging under MIT license. An archival snapshot of the version used in this study is deposited on Zenodo at https://doi.org/10.5281/zenodo.20338561. Implemented in Python 3.12+ with PyTorch 2.6 and DSPY 3.0, supporting Linux.

Large language models (LLMs) have demonstrated extensive capabilities across various natural language processing (NLP) tasks. Knowledge graphs (KGs) harbor vast amounts of facts, furnishing external knowledge for language models. The structured knowledge extracted from KGs must undergo conversion into sentences to align with the input format required by LLMs. Previous research has commonly utilized methods such as triple conversion and template-based conversion. However, sentences converted using existing methods frequently encounter issues such as semantic incoherence, ambiguity, and unnaturalness, which distort the original intent, and deviate the sentences from the facts. Meanwhile, despite the improvement that knowledge-enhanced pre-training and prompt-tuning methods have achieved in small-scale models, they are difficult to implement for LLMs in the absence of computational resources. The advanced comprehension of LLMs facilitates in-context learning (ICL), thereby enhancing their performance without the need for additional training. In this paper, we propose a knowledge prompts generation method, GenKP, which injects knowledge into LLMs by ICL. Compared to inserting triple-conversion or templated-conversion knowledge without selection, GenKP entails generating knowledge samples using LLMs in conjunction with KGs and makes a trade-off of knowledge samples through weighted verification and BM25 ranking, reducing knowledge noise. Experimental results illustrate that incorporating knowledge prompts enhances the performance of LLMs. Furthermore, LLMs augmented with GenKP exhibit superior improvements compared to the methods utilizing triple and template-based knowledge injection.

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.

Temporal relation annotation in the clinical domain is crucial yet challenging due to its workload and the medical expertise required. In this paper, we propose a novel annotation method that integrates event start-points ordering and question-answering (QA) as the annotation format. By focusing only on two points on a timeline, start-points ordering reduces ambiguity and simplifies the relation set to be considered during annotation. QA as annotation recasts temporal relation annotation into a reading comprehension task, allowing annotators to use natural language instead of the formalisms commonly adopted in temporal relation annotation. Based on our method, most of the relations in a document are inferable from a significantly smaller number of explicitly annotated relations, showing the efficiency of our proposed method. Using these inferred relations, we develop a temporal relation classification model that achieves a 0.72 F1 score. Also, by decomposing the annotation process into QA generation and QA validation, our method enables collaboration among medical experts and non-experts. We obtained high inter-annotator agreement (IAA) scores, which indicate the positive prospect of such collaboration in the annotation process. Our annotated corpus, annotation tool, and trained model are publicly available: https://github.com/seiji-shimizu/qa-start-ordering.

Entity Linking performance has a strong reliance on having a large quantity of high-quality annotated training data available.Yet, manual annotation of named entities, especially their boundaries, is ambiguous, error-prone, and raises many inconsistencies between annotators.While imprecise boundary annotation can degrade a model's performance, there are applications where accurate extraction of entities' surface form is not necessary.For those cases, a lenient annotation guideline could relieve the annotators' workload and speed up the process.This paper presents a case study designed to verify the feasibility of such annotation process and evaluate the impact of boundary-relaxed annotation in an Entity Linking pipeline.We first generate a set of noisy versions of the widely used AIDA CoNLL-YAGO dataset by expanding the boundaries subsets of annotated entity mentions and then train three Entity Linking models on this data and evaluate the relative impact of imprecise annotation on entity recognition and disambiguation performances.We demonstrate that the magnitude of effects caused by noise in the Named Entity Recognition phase is dependent on both model complexity and noise ratio, while Entity Disambiguation components are susceptible to entity boundary imprecision due to strong vocabulary dependency.

Abstract Album art often reflects the trends and themes of the songs in a given collection, and even the identities of the musicians who produced it. It therefore plays a central role in fomenting a potential listener’s first impression of the work. As such, musicians strive to find suitable images for this purpose, and those with limited financial resources or design skills may struggle to do so. Here, we report the development of Visualyre, a deep learning–based application that generates album art images from users’ song lyrics and audio files. This tool relies on generative adversarial network models to generate images from textual input (lyrics) and style transfer models to adjust the image according to the mood of the audio. We then report the results of a user study involving 35 amateur and independent musicians who tested the system. Results suggest that Visualyre was generally well received and largely effective in its intended purpose: providing musicians with a resource for generating their own album art.

In the field of Japanese medical information extraction, few analyzing tools are available and relation extraction is still an under-explored topic. In this paper, we first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports. We experiment with the practical annotation scenarios by separately annotating two different types of reports. We design a pipeline system with three components for recognizing medical entities, classifying entity modalities, and extracting relations. The empirical results show accurate analyzing performance and suggest the satisfactory annotation quality, the superiority of the latest contextual embedding models. and the feasible annotation strategy for high-accuracy demand.

This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time.Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions.We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful).To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning).We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity.Our results show that the transfer of features from social media to live streaming is effective.However, the two measurements show less correlation in the transferability prediction.

Applying natural language processing (NLP) to medical and clinical texts can bring important social benefits by mining valuable information from unstructured text. A popular application for that purpose is named entity recognition (NER), but the annotation policies of existing clinical corpora have not been standardized across clinical texts of different types. This paper presents an annotation guideline aimed at covering medical documents of various types such as radiography interpretation reports and medical records. Furthermore, the annotation was designed to avoid burdensome requirements related to medical knowledge, thereby enabling corpus development without medical specialists. To achieve these design features, we specifically focus on critical lung diseases to stabilize linguistic patterns in corpora. After annotating around 1100 electronic medical records following the annotation scheme, we demonstrated its feasibility using an NER task. Results suggest that our guideline is applicable to large-scale clinical NLP projects.