Linguistic Rule-Based Ontology-Driven Chatbot System

Saini, Anuj; Verma, Aayushi; Arora, Anuja; Gupta, Chetan

doi:10.1007/978-981-13-0344-9_4

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…A common approach towards NLU is the rule-based method, where domain experts define the semantic frames, design a set of heuristic rules to locate intents and slots (Saini et al 2019). However, the grammar rules have limitations in modelling the complex structures of real-world dialogs as they are based on the syntactic, semantic and discourse information of the utterances.…”

Section: Introductionmentioning

confidence: 99%

Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding

Zhou¹,

Feng²,

Wu³

et al. 2022

Preprint

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Popular approaches for Natural Language Understanding (NLU) usually rely on a huge amount of annotated data or handcrafted rules, which is laborious and not adaptive to domain extension. We recently proposed a Convex-Polytopic-Model-based framework that shows great potential in automatically extracting semantic patterns by exploiting the raw dialog corpus. The extracted semantic patterns can be used to generate semantic frames, which is essential in assisting NLU tasks. This paper further studies the CPM model in depth and visualizes its high interpretability and transparency at various levels. We show that this framework can exploit semantic-frame-related features in the corpus, reveal the underlying semantic structure of the utterances, and boost the performance of the state-of-the-art NLU model with minimal supervision. We conduct our experiments on the ATIS (Air Travel Information System) corpus.

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Section: Introductionmentioning

confidence: 99%

Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding

Zhou¹,

Feng²,

Wu³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Identification of Conversational Intent Pattern Using Pattern-Growth Technique for Academic Chatbot

Alias

Sainin

Fun

et al. 2019

Lecture Notes in Computer Science

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Transparent, Low Resource, and Context-Aware Information Retrieval From a Closed Domain Knowledge Base

Rateria,

Singh

2024

IEEE Access

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In large-scale enterprises, vast quantities of textual information are harbored across corporate repositories and intranet websites. Traditional search techniques, lacking context sensitivity, often need to improve in efficiently retrieving pertinent data. Modern techniques that use a distributed representation of words require a considerable training dataset and computation, thus presenting a financial and operational burden. Generative models for information search suffer from problems of transparency and hallucination, which can be detrimental, especially for organizations and their stakeholders who rely on these results for critical business operations. This paper presents a non-goal-oriented conversational agent based on a collection of finite state machines and an information search model for text search from an extensive collection of stored corporate documents and intranet websites. We use a distributed representation of words derived from the BERT model, which allows for contextual searches. We minimally fine-tune a BERT model on a multi-label text classification task specific to a closed-domain knowledge base. Based on DCG metrics, our information retrieval model using distributed embeddings from the minimally trained BERT model and Word Movers Distance for calculating topic similarity shows a higher relevance to user queries than BERT embeddings with cosine similarity and BM25. Our architecture promises to drastically expedite and improve the accuracy of information retrieval in closed-domain systems without the need for a massive training dataset or expensive computing while maintaining transparency.

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Linguistic Rule-Based Ontology-Driven Chatbot System

Cited by 4 publications

References 12 publications

Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding

Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding

Identification of Conversational Intent Pattern Using Pattern-Growth Technique for Academic Chatbot

Transparent, Low Resource, and Context-Aware Information Retrieval From a Closed Domain Knowledge Base

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