Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis

This paper fills a gap in aspect-based sentiment analysis and aims to present a new method for preparing and analysing texts concerning opinion and generating user-friendly descriptive reports in natural language. We present a comprehensive set of techniques derived from Rhetorical Structure Theory and sentiment analysis to extract aspects from textual opinions and then build an abstractive summary of a set of opinions. Moreover, we propose aspect-aspect graphs to evaluate the importance of aspects and to filter out unimportant ones from the summary. Additionally, the paper presents a prototype solution of data flow with interesting and valuable results. The proposed method's results proved the high accuracy of aspect detection when applied to the gold standard dataset

Thread-level information for comment classification in community question answering

Community Question Answering (cQA) is a new application of QA in social contexts (e.g., fora). It presents new interesting challenges and research directions, e.g., exploiting the dependencies between the different comments of a thread to select the best answer for a given question. In this paper, we explored two ways of modeling such dependencies: (i) by designing specific features looking globally at the thread; and (ii) by applying structure prediction models. We trained and evaluated our models on data from SemEval-2015 Task 3 on Answer Selection in cQA. Our experiments show that: (i) the thread-level features consistently improve the performance for a variety of machine learning models, yielding state-of-the-art results; and (ii) sequential dependencies between the answer labels captured by structured prediction models are not enough to improve the results, indicating that more information is needed in the joint model

DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text

Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when solely relying on their internal knowledge, especially when answering questions that require less commonly known information. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge. Nonetheless, recent approaches have primarily emphasized retrieval from unstructured text corpora, owing to its seamless integration into prompts. When using structured data such as knowledge graphs, most methods simplify it into natural text, neglecting the underlying structures. Moreover, a significant gap in the current landscape is the absence of a realistic benchmark for evaluating the effectiveness of grounding LLMs on heterogeneous knowledge sources (e.g., knowledge base and text). To fill this gap, we have curated a comprehensive dataset that poses two unique challenges: (1) Two-hop multi-source questions that require retrieving information from both open-domain structured and unstructured knowledge sources; retrieving information from structured knowledge sources is a critical component in correctly answering the questions. (2) The generation of symbolic queries (e.g., SPARQL for Wikidata) is a key requirement, which adds another layer of challenge. Our dataset is created using a combination of automatic generation through predefined reasoning chains and human annotation. We also introduce a novel approach that leverages multiple retrieval tools, including text passage retrieval and symbolic language-assisted retrieval. Our model outperforms previous approaches by a significant margin, demonstrating its effectiveness in addressing the above-mentioned reasoning challenges

Comparative study between breast conservative surgery and modified radical mastectomy in early stage of breast carcinoma in a tertiary care hospital

Background: Breast cancer is the most prevalent cancer in women globally, with two million new cases and more than half a million deaths each year. Surgery is the key component of treating breast cancer and there are two primary types of breast surgery available: breast conservative surgery and modified radical mastectomy. The aim of this study was to compare BCS and MRM in the treatment of early-stage breast carcinoma. Methods: This was a prospective observational study that involved 74 patients and was carried out in the Department of Surgery at Shaheed Suhrawardy Medical College & Hospital and Enam Medical College & Hospital with an 18-months minimum follow-up. The time frame for inclusion was from July 2018 through July 2020. There were two patient groups, 37 patients in Group A who underwent breast conservative surgery and Group B was made up of 37 individuals who had MRM for early-stage breast carcinoma. Results: With a mean age of 47.65 years in the BCS group and 48.19 years in the MRM group, the operative time for BCS was 1.04±0.25 hours, whereas 3.20±0.48 hours for MRM. Statistically significant higher amount of post-operative drainage volume in MRM group compared to BCS group (p value=0.000). With an excellent aesthetic outcome rate in BCS group (p value<0.0001) as well as better quality of life than MRM group. Conclusions: Breast conservative surgery and modified radical mastectomy are both oncologically safe treatments for early-stage breast cancer with multidisciplinary approach. BCS offers less trauma, infection and hospital stay; better aesthetic outcome and quality of life than MRM, making it more deserving of being promoted clinically in the treatment of early-stage breast cancer

Mind your inflections! Improving NLP for non-standard Englishes with base-inflection encoding

10.18653/v1/2020.emnlp-main.455Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)5647-566

PIANO: influence maximization meets deep reinforcement learning

Since its introduction in 2003, the influence maximization (IM) problem has drawn significant research attention in the literature. The aim of IM, which is NP-hard, is to select a set of k users known as seed users who can influence the most individuals in the social network. The state-of-the-art algorithms estimate the expected influence of nodes based on sampled diffusion paths. As the number of required samples has been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and the efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency. In this article, we present an orthogonal and novel paradigm to address the IM problem by leveraging deep reinforcement learning (RL) to estimate the expected influence. In particular, we present a novel framework called deeP reInforcement leArning-based iNfluence maximizatiOn (PIANO) that incorporates network embedding and RL techniques to address this problem. In order to make it practical, we further present PIANO-E and PIANO@⟨angle d⟩, both of which can be applied directly to answer IM without training the model from scratch. Experimental study on real-world networks demonstrates that PIANO achieves the best performance with respect to efficiency and influence spread quality compared to state-of-the-art classical solutions. We also demonstrate that the learned parametric models generalize well across different networks. Besides, we provide a pool of pretrained PIANO models such that any IM task can be addressed by directly applying a model from the pool without training over the targeted network.This work was supported by the National Natural Science Foundation of China under Grant 61972309