Mining Event - Based Commonsense Knowledge from Web using NLP Techniques

The real life intelligent applications such as agents, expert systems, dialog understanding systems, weather forecasting systems, robotics etc. mainly focus on commonsense knowledge And basically these works on the knowledgebase which contains large amount of commonsense knowledge. The main intention of this work is to create a commonsense knowledge base by using an effective methodology to retrieve commonsense knowledge from large amount of web data. In order to achieve the best results, it makes use of different natural language processing techniques such as semantic role labeling, lexical and syntactic analysi. Keywords: Automatic statistical semantic role tagger (ASSERT), lexico - syntactic pattern matching, semantic role labeling (SRL

MEBCK from Web using NLP Techniques

The real life intelligent applications such as agents, expert systems, dialog understanding systems, weather forecasting systems, robotics etc. mainly focus on commonsense knowledge And basically these works on the knowledgebase which contains large amount of commonsense knowledge. The main intention of this work is to create a commonsense knowledge base by using an effective methodology to retrieve commonsense knowledge from large amount of web data. In order to achieve the best results, it makes use of different natural language processing techniques such as semantic role labeling, lexical and syntactic analysis. Keywords: Automatic statistical semantic role tagger (ASSERT), lexico - syntactic pattern matching, semantic role labeling (SRL

Commonsense Knowledge Base Construction in the Age of Big Data

Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific interest to the data management community. (i) We use Quasimodo to illustrate knowledge extraction systems engineering, (ii) Dice to illustrate the role that schema constraints play in cleaning fuzzy commonsense knowledge, and (iii) Ascent to illustrate the relevance of conceptual modelling. The demos are available online at https://quasimodo.r2.enst.fr, https://dice.mpi-inf.mpg.de and ascent.mpi-inf.mpg.de

Lifted rule injection for relation embeddings

Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how to efficiently incorporate commonsense knowledge into such models. A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules. However, propositionalization does not scale beyond domains with only few entities and rules. In this paper we present a highly efficient method for incorporating implication rules into distributed representations for automated knowledge base construction. We map entity-tuple embeddings into an approximately Boolean space and encourage a partial ordering over relation embeddings based on implication rules mined from WordNet. Surprisingly, we find that the strong restriction of the entity-tuple embedding space does not hurt the expressiveness of the model and even acts as a regularizer that improves generalization. By incorporating few commonsense rules, we achieve an increase of 2 percentage points mean average precision over a matrix factorization baseline, while observing a negligible increase in runtime

Commonsense Reasoning for Conversational AI: A Survey of the State of the Art

Large, transformer-based pretrained language models like BERT, GPT, and T5 have demonstrated a deep understanding of contextual semantics and language syntax. Their success has enabled significant advances in conversational AI, including the development of open-dialogue systems capable of coherent, salient conversations which can answer questions, chat casually, and complete tasks. However, state-of-the-art models still struggle with tasks that involve higher levels of reasoning - including commonsense reasoning that humans find trivial. This paper presents a survey of recent conversational AI research focused on commonsense reasoning. The paper lists relevant training datasets and describes the primary approaches to include commonsense in conversational AI. The paper also discusses benchmarks used for evaluating commonsense in conversational AI problems. Finally, the paper presents preliminary observations of the limited commonsense capabilities of two state-of-the-art open dialogue models, BlenderBot3 and LaMDA, and its negative effect on natural interactions. These observations further motivate research on commonsense reasoning in conversational AI.Comment: Accepted to Workshop on Knowledge Augmented Methods for Natural Language Processing, in conjunction with AAAI 202