USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset

Sentiment analysis is a pivotal task in the domain of natural language processing. It encompasses both text-level sentiment polarity classification and word-level Part of Speech(POS) sentiment polarity determination. Such analysis challenges models to understand text holistically while also extracting nuanced information. With the rise of Large Language Models(LLMs), new avenues for sentiment analysis have opened. This paper proposes enhancing performance by leveraging the Mutual Reinforcement Effect(MRE) between individual words and the overall text. It delves into how word polarity influences the overarching sentiment of a passage. To support our research, we annotated four novel Sentiment Text Classification and Part of Speech(SCPOS) datasets, building upon existing sentiment classification datasets. Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a 7-billion parameter size. Experimental results revealed that our model surpassed the performance of gpt-3.5-turbo across all four datasets, underscoring the significance of MRE in sentiment analysis.

Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks

Information extraction(IE) is a crucial subfield within natural language processing. However, for the traditionally segmented approach to sentence classification and Named Entity Recognition, the intricate interactions between these individual subtasks remain largely uninvestigated. In this study, we propose an integrative analysis, converging sentence classification with Named Entity Recognition, with the objective to unveil and comprehend the mutual reinforcement effect within these two information extraction subtasks. To achieve this, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia dataset containing both SC and NER. Using a format converter, we unify input formats and employ a generative model to generate SC-labels, NER-labels, and associated text segments. We propose a Constraint Mechanism (CM) to improve generated format accuracy. Our results show SC accuracy increased by 1.13 points and NER by 1.06 points in SCNM compared to standalone tasks, with CM raising format accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects between SC and NER, and integration enhances both tasks' performance. We additionally implemented the SLG framework on single SC task. It yielded superior accuracies compared to the baseline on two distinct Japanese SC datasets. Notably, in the experiment of few-shot learning, SLG framework shows much better performance than fine-tune method. These empirical findings contribute additional evidence to affirm the efficacy of the SLG framework.

Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition

Information extraction(IE) is a crucial subfield within natural language processing. In this study, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia dataset containing both SC and NER. Using a format converter, we unify input formats and employ a generative model to generate SC-labels, NER-labels, and associated text segments. We propose a Constraint Mechanism (CM) to improve generated format accuracy. Our results show SC accuracy increased by 1.13 points and NER by 1.06 points in SCNM compared to standalone tasks, with CM raising format accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects between SC and NER, and integration enhances both tasks' performance.

Sensitivity and Robustness of Large Language Models to Prompt in Japanese

Prompt Engineering has gained significant relevance in recent years, fueled by advancements in pre-trained and large language models. However, a critical issue has been identified within this domain: the lack of sensitivity and robustness of these models towards Prompt Templates, particularly in lesser-studied languages such as Japanese. This paper explores this issue through a comprehensive evaluation of several representative Large Language Models (LLMs) and a widely-utilized pre-trained model(PLM), T5. These models are scrutinized using a benchmark dataset in Japanese, with the aim to assess and analyze the performance of the current multilingual models in this context. Our experimental results reveal startling discrepancies. A simple modification in the sentence structure of the Prompt Template led to a drastic drop in the accuracy of GPT-4 from 49.21 to 25.44. This observation underscores the fact that even the highly performance GPT-4 model encounters significant stability issues when dealing with diverse Japanese prompt templates, rendering the consistency of the model's output results questionable. In light of these findings, we conclude by proposing potential research trajectories to further enhance the development and performance of Large Language Models in their current stage.

A Few-shot Approach to Resume Information Extraction via Prompts

Prompt learning has been shown to achieve near-Fine-tune performance in most text classification tasks with very few training examples. It is advantageous for NLP tasks where samples are scarce. In this paper, we attempt to apply it to a practical scenario, i.e resume information extraction, and to enhance the existing method to make it more applicable to the resume information extraction task. In particular, we created multiple sets of manual templates and verbalizers based on the textual characteristics of resumes. In addition, we compared the performance of Masked Language Model (MLM) pre-training language models (PLMs) and Seq2Seq PLMs on this task. Furthermore, we improve the design method of verbalizer for Knowledgeable Prompt-tuning in order to provide a example for the design of Prompt templates and verbalizer for other application-based NLP tasks. In this case, we propose the concept of Manual Knowledgeable Verbalizer(MKV). A rule for constructing the Knowledgeable Verbalizer corresponding to the application scenario. Experiments demonstrate that templates and verbalizers designed based on our rules are more effective and robust than existing manual templates and automatically generated prompt methods. It is established that the currently available automatic prompt methods cannot compete with manually designed prompt templates for some realistic task scenarios. The results of the final confusion matrix indicate that our proposed MKV significantly resolved the sample imbalance issue.

Construction of English Resume Corpus and Test with Pre-trained Language Models

Information extraction(IE) has always been one of the essential tasks of NLP. Moreover, one of the most critical application scenarios of information extraction is the information extraction of resumes. Constructed text is obtained by classifying each part of the resume. It is convenient to store these texts for later search and analysis. Furthermore, the constructed resume data can also be used in the AI resume screening system. Significantly reduce the labor cost of HR. This study aims to transform the information extraction task of resumes into a simple sentence classification task. Based on the English resume dataset produced by the prior study. The classification rules are improved to create a larger and more fine-grained classification dataset of resumes. This corpus is also used to test some current mainstream Pre-training language models (PLMs) performance.Furthermore, in order to explore the relationship between the number of training samples and the correctness rate of the resume dataset, we also performed comparison experiments with training sets of different train set sizes.The final multiple experimental results show that the resume dataset with improved annotation rules and increased sample size of the dataset improves the accuracy of the original resume dataset.