Natural Language Processing in the Legal Domain

Katz, Daniel; Hartung, Dirk; Gerlach, Lauritz; Jana, Abhik; Bommarito, Michael James

doi:10.48550/arxiv.2302.12039

Cited by 3 publications

(1 citation statement)

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“…Tackling slang language on Twitter is essential for advancing NLP and DL technologies [15]. However, there is still a challenge with the specifics of the text that needs to be processed when it comes to matching texts with slang phrases [16]. The author of a given text may alter the original terminology by employing synonymous language within their sentences.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Emotion Classification Scheme for Twits Based on Deep Learning Approach

Sultan

2023

RIA

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Given the widespread incorporation of social media into everyday existence, platforms such as Twitter have become crucial arenas for individuals to articulate their thoughts, emotions, and viewpoints. The ability to identify emotions in these facial expressions has a wide range of practical uses, including tailored marketing strategies and research on human behaviour. Nevertheless, the language used on these platforms is frequently filled with colloquialisms and vagueness, rendering the process of detecting emotions a challenging endeavour even for individuals. The difficulties in analysing emotions on Twitter are particularly noticeable because current natural language processing (NLP) techniques have limited ability to handle slang language, and earlier classifiers that rely on slang have produced unsatisfactory results. This paper presents a new model for categorising emotions in tweets that contain slang. The model combines multiple approaches and utilises the WordNET dataset. The WordNET library is used in the proposed model to create synonymous phrases for the sentences in the text. The text data is divided into segments, reduced to their root forms, then filtered to remove often used words using natural language processing (NLP) approaches. The textual properties are described by utilising Term Frequency -Inverse Document Frequency (TF-IDF) and n-gram based similarity approaches. Emotions are classified by the utilisation of a convolutional neural network (CNN). An analysis was conducted on the performance of this model based on the metrics of classification accuracy and processing speed. The findings revealed a remarkable level of accuracy in classifying emotions in slang language using the suggested model, achieving a precise categorization rate of 89.3%. This study represents a notable advancement in the field of emotion classification in social media writing that contains slang language.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Emotion Classification Scheme for Twits Based on Deep Learning Approach

Sultan

2023

RIA

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Natural Language Processing (NLP)-Powered Legal A(t)Ms (LAMs) in India: Possibilities and Challenges

Alexopoulos

Saxena

2023

J Knowl Econ

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Performance analysis of large language models in the domain of legal argument mining

Al Zubaer,

Granitzer,

Mitrović

2023

Front. Artif. Intell.

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Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.

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Natural Language Processing in the Legal Domain

Cited by 3 publications

References 50 publications

An Enhanced Emotion Classification Scheme for Twits Based on Deep Learning Approach

An Enhanced Emotion Classification Scheme for Twits Based on Deep Learning Approach

Natural Language Processing (NLP)-Powered Legal A(t)Ms (LAMs) in India: Possibilities and Challenges

Performance analysis of large language models in the domain of legal argument mining

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