Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework.

In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple emojis in English-Hindi code-mixed sentences and proposes a new dataset called SENTIMOJI, which extends the SemEval 2020 Task 9 SentiMix dataset.

Neural response generation for task completion using conversational knowledge graph.

Effective dialogue generation for task completion is challenging to build. The task requires the response generation system to generate the responses consistent with intent and slot values, have diversity in response and be able to handle multiple domains. The response also needs to be context relevant with respect to the previous utterances in the conversation.

Symptoms are known by their companies: towards association guided disease diagnosis assistant.

Over the last few years, dozens of healthcare surveys have shown a shortage of doctors and an alarming doctor-population ratio. With the motivation of assisting doctors and utilizing their time efficiently, automatic disease diagnosis using artificial intelligence is experiencing an ever-growing demand and popularity. Humans are known by the company they keep; similarly, symptoms also exhibit the association property, i.

: a novel resource for question answering on scholarly articles.

Machine Reading Comprehension (MRC) of a document is a challenging problem that requires discourse-level understanding. Information extraction from scholarly articles nowadays is a critical use case for researchers to understand the underlying research quickly and move forward, especially in this age of infodemic. MRC on research articles can also provide helpful information to the reviewers and editors.

Investigating the impact of emotion on temporal orientation in a deep multitask setting.

Temporal orientation is an important aspect of human cognition which shows how an individual emphasizes past, present, and future. Theoretical research in psychology shows that one's emotional state can influence his/her temporal orientation. We hypothesize that measuring human temporal orientation can benefit from concurrent learning of emotion.

A dynamic goal adapted task oriented dialogue agent.

Purpose: Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e.

Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning.

Purpose: Developing a Dialogue/Virtual Agent (VA) that can handle complex tasks (need) of the user pertaining to multiple intents of a domain is challenging as it requires the agent to simultaneously deal with multiple subtasks. However, majority of these end-to-end dialogue systems incorporate only user semantics as inputs in the learning process and ignore other useful user behavior and information. Sentiment of the user at the time of conversation plays an important role in securing maximum user gratification.

Extractive single document summarization using binary differential evolution: Optimization of different sentence quality measures.

With the increase in the amount of text information in different real-life applications, automatic text-summarization systems become more predominant in extracting relevant information. In the current study, we formulated the problem of extractive text-summarization as a binary optimization problem, and multi-objective binary differential evolution (DE) based optimization strategy is employed to solve this. The solutions of DE encode a possible subset of sentences to be present in the summary which is then evaluated based on some statistical features (objective functions) namely, the position of the sentence in the document, the similarity of a sentence with the title, length of the sentence, cohesion, readability, and coverage.

Statistical-based system combination approach to gain advantages over different machine translation systems.

Every machine translation system has some advantages. We propose an improved statistical system combination approach to achieve the advantages of existing machine translation systems. The primary task is to score all the phrases of the outputs of different machine translation systems selected for combination.

Resolution of grammatical tense into actual time, and its application in Time Perspective study in the tweet space.

Time Perspective (TP) is an important area of research within the 'psychological time' paradigm. TP, or the manner in which individuals conduct themselves as a reflection of their cogitation of the past, the present, and the future, is considered as a basic facet of human functioning. These perceptions of time have an influence on our actions, perceptions, and emotions.

Death by design: where curcumin sensitizes drug-resistant tumours.

Chemotherapy remains the core of anticancer treatment. However, despite the tremendous strides made in the development of targeted anticancer therapies, emergence of resistance to chemotherapeutic drugs is still a major obstacle in the successful management of resistant tumours. Therefore, profound investigation into the in-depth molecular mechanisms of drug resistance is essential and may hopefully translate into effective therapies that can flip the switch from drug resistance to susceptibility.