3 research outputs found
Encoder-Attention-Based Automatic Term Recognition (EA-ATR)
No full textAutomated Term Recognition (ATR) is the task of finding terminology from raw text. It involves designing and developing techniques for the mining of possible terms from the text and filtering these identified terms based on their scores calculated using scoring methodologies like frequency of occurrence and then ranking the terms. Current approaches often rely on statistics and regular expressions over part-of-speech tags to identify terms, but this is error-prone. We propose a deep learning technique to improve the process of identifying a possible sequence of terms. We improve the term recognition by using Bidirectional Encoder Representations from Transformers (BERT) based embeddings to identify which sequence of words is a term. This model is trained on Wikipedia titles. We assume all Wikipedia titles to be the positive set, and random n-grams generated from the raw text as a weak negative set. The positive and negative set will be trained using the Embed, Encode, Attend and Predict (EEAP) formulation using BERT as embeddings. The model will then be evaluated against different domain-specific corpora like GENIA - annotated biological terms and Krapivin - scientific papers from the computer science domain
LUCE: a dynamic framework and interactive dashboard for opinionated text analysis
No full textWe introduce LUCE, an advanced dynamic framework with an interactive dashboard for analysing opinionated text aiming to understand people-centred communication. The framework features computational modules of text classification and extraction explicitly designed for analysing different elements of opinions, e.g., sentiment/emotion, suggestion, figurative language, hate/toxic speech, and topics. We designed the framework using a modular architecture, allowing scalability and extensibility with the aim of supporting other NLP tasks in subsequent versions. LUCE comprises trained models, python-based APIs, and a user-friendly dashboard, ensuring an intuitive user experience. LUCE has been validated in a relevant environment, and its capabilities and performance have been demonstrated through initial prototypes and pilot studies.This publication has emanated from research con ducted with the financial support of Taighde Éire ann – Research Ireland for the Postdoctoral Fel lowship award GOIPD/2023/1556 (Glór). This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883285 (PANDEM-2). Additionally, this work has been partially funded by the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2 (Insight_2).peer-reviewe
Intent Classification by the Use of Automatically Generated Knowledge Graphs
No full textIntent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers¿ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs for targeted data sets to capture domain-specific knowledge and leverage embeddings trained on these knowledge graphs for the intent classification task. As existing knowledge graphs might not be suitable for a targeted domain of interest, our automatic generation of knowledge graphs can extract the semantic information of any domain, which can be incorporated within the classification process. We compare our results with state-of-the-art pre-trained sentence embeddings and our evaluation of three data sets shows improvement in the intent classification task in terms of precision.This publication has emanated from research supported in part by a grant from Science Foundation Ireland under Grant number SFI/12/RC/2289_P2. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.peer-reviewe
