64 research outputs found
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
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect
Information Extraction (IE) stands as a cornerstone in natural language
processing, traditionally segmented into distinct sub-tasks. The advent of
Large Language Models (LLMs) heralds a paradigm shift, suggesting the
feasibility of a singular model addressing multiple IE subtasks. In this vein,
we introduce the General Information Extraction Large Language Model (GIELLM),
which integrates text Classification, Sentiment Analysis, Named Entity
Recognition, Relation Extraction, and Event Extraction using a uniform
input-output schema. This innovation marks the first instance of a model
simultaneously handling such a diverse array of IE subtasks. Notably, the
GIELLM leverages the Mutual Reinforcement Effect (MRE), enhancing performance
in integrated tasks compared to their isolated counterparts. Our experiments
demonstrate State-of-the-Art (SOTA) results in five out of six Japanese mixed
datasets, significantly surpassing GPT-3.5-Turbo. Further, an independent
evaluation using the novel Text Classification Relation and Event
Extraction(TCREE) dataset corroborates the synergistic advantages of MRE in
text and word classification. This breakthrough paves the way for most IE
subtasks to be subsumed under a singular LLM framework. Specialized fine-tune
task-specific models are no longer needed.Comment: 10 pages, 6 figure
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.Comment: Accept in NLDB2023 as Long Pape
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.Comment: 25 pages, 12 figures, 19 tables. arXiv admin note: substantial text
overlap with arXiv:2306.1597
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.Comment: Model already Open Sourced, Dataset will release soo
Introduction of a Probabilistic Language Model to Non-Factoid Question-Answering Using Example Q&A Pairs
Mediatory Summary Generation: Summary-Passage Extraction for Information Credibility on the Web
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Utilization of histories by country in question-answering system to solve world history essay type questions
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