Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing

[EN] In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.The work of Paolo Rosso was partially funded by the Spanish MICINN under the project PGC2018-096212-B-C31.Distante, D.; Faralli, S.; Rittinghaus, S.; Rosso, P.; Samsami, N. (2022). DomainSenticNet: An Ontology and a Methodology Enabling Domain-aware Sentic Computing. Cognitive Computation. 14(1):62-77. https://doi.org/10.1007/s12559-021-09825-w627714

Script acquisition : a crowdsourcing and text mining approach

According to Grice’s (1975) theory of pragmatics, people tend to omit basic information when participating in a conversation (or writing a narrative) under the assumption that left out details are already known or can be inferred from commonsense knowledge by the hearer (or reader). Writing and understanding of texts makes particular use of a specific kind of common-sense knowledge, referred to as script knowledge. Schank and Abelson (1977) proposed Scripts as a model of human knowledge represented in memory that stores the frequent habitual activities, called scenarios, (e.g. eating in a fast food restaurant, etc.), and the different courses of action in those routines. This thesis addresses measures to provide a sound empirical basis for high-quality script models. We work on three key areas related to script modeling: script knowledge acquisition, script induction and script identification in text. We extend the existing repository of script knowledge bases in two different ways. First, we crowdsource a corpus of 40 scenarios with 100 event sequence descriptions (ESDs) each, thus going beyond the size of previous script collections. Second, the corpus is enriched with partial alignments of ESDs, done by human annotators. The crowdsourced partial alignments are used as prior knowledge to guide the semi-supervised script-induction algorithm proposed in this dissertation. We further present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets and inducing their temporal order. The proposed semi-supervised clustering model better handles order variation in scripts and extends script representation formalism, Temporal Script graphs, by incorporating "arbitrary order" equivalence classes in order to allow for the flexible event order inherent in scripts. In the third part of this dissertation, we introduce the task of scenario detection, in which we identify references to scripts in narrative texts. We curate a benchmark dataset of annotated narrative texts, with segments labeled according to the scripts they instantiate. The dataset is the first of its kind. The analysis of the annotation shows that one can identify scenario references in text with reasonable reliability. Subsequently, we proposes a benchmark model that automatically segments and identifies text fragments referring to given scenarios. The proposed model achieved promising results, and therefore opens up research on script parsing and wide coverage script acquisition.Gemäß der Grice’schen (1975) Pragmatiktheorie neigen Menschen dazu, grundlegende Informationen auszulassen, wenn sie an einem Gespräch teilnehmen (oder eine Geschichte schreiben). Dies geschieht unter der Annahme, dass die ausgelassenen Details bereits bekannt sind, oder vom Hörer (oder Leser) aus Weltwissen erschlossen werden können. Besonders beim Schreiben und Verstehen von Text wird Verwendung einer spezifischen Art von solchem Weltwissen gemacht, welches auch Skriptwissen genannt wird. Schank und Abelson (1977) erdachten Skripte als ein Modell menschlichen Wissens, welches im menschlichen Gedächtnis gespeichert ist und häufige Alltags-Aktivitäten sowie deren typischen Ablauf beinhaltet. Solche Skript-Aktivitäten werden auch als Szenarios bezeichnet und umfassen zum Beispiel Im Restaurant Essen etc. Diese Dissertation widmet sich der Bereitstellung einer soliden empirischen Grundlage zur Akquisition qualitativ hochwertigen Skriptwissens. Wir betrachten drei zentrale Aspekte im Bereich der Skriptmodellierung: Akquisition ition von Skriptwissen, Skript-Induktion und Skriptidentifizierung in Text. Wir erweitern das bereits bestehende Repertoire und Skript-Datensätzen in 2 Bereichen. Erstens benutzen wir Crowdsourcing zur Erstellung eines Korpus, das 40 Szenarien mit jeweils 100 Ereignissequenzbeschreibungen (Event Sequence Descriptions, ESDs) beinhaltet, und welches somit größer als bestehende Skript- Datensätze ist. Zweitens erweitern wir das Korpus mit partiellen ESD-Alignierungen, die von Hand annotiert werden. Die partiellen Alignierungen werden dann als Vorwissen für einen halbüberwachten Algorithmus zur Skriptinduktion benutzt, der im Rahmen dieser Dissertation vorgestellt wird. Wir präsentieren außerdem einen halbüberwachten Clusteringansatz zur Induktion von Skripten, basierend auf Ereignissequenzen, die via Crowdsourcing gesammelt wurden. Hierbei werden einzelne Ereignisbeschreibungen gruppiert, um Paraphrasenmengen und der deren temporale Ordnung abzuleiten. Der vorgestellte Clusteringalgorithmus ist im Stande, Variationen in der typischen Reihenfolge in Skripte besser abzubilden und erweitert damit einen Formalismus zur Skriptrepräsentation, temporale Skriptgraphen. Dies wird dadurch bewerkstelligt, dass Equivalenzklassen von Beschreibungen mit "arbiträrer Reihenfolge" genutzt werden, die es erlauben, eine flexible Ereignisordnung abzubilden, die inhärent bei Skripten vorhanden ist. Im dritten Teil der vorliegenden Arbeit führen wir den Task der SzenarioIdentifikation ein, also der automatischen Identifikation von Skriptreferenzen in narrativen Texten. Wir erstellen einen Benchmark-Datensatz mit annotierten narrativen Texten, in denen einzelne Segmente im Bezug auf das Skript, welches sie instantiieren, markiert wurden. Dieser Datensatz ist der erste seiner Art. Eine Analyse der Annotation zeigt, dass Referenzen zu Szenarien im Text mit annehmbarer Akkuratheit vorhergesagt werden können. Zusätzlich stellen wir ein Benchmark-Modell vor, welches Textfragmente automatisch erstellt und deren Szenario identifiziert. Das vorgestellte Modell erreicht erfolgversprechende Resultate und öffnet damit einen Forschungszweig im Bereich des Skript-Parsens und der Skript-Akquisition im großen Stil

The Life Cycle of Knowledge in Big Language Models: A Survey

Knowledge plays a critical role in artificial intelligence. Recently, the extensive success of pre-trained language models (PLMs) has raised significant attention about how knowledge can be acquired, maintained, updated and used by language models. Despite the enormous amount of related studies, there still lacks a unified view of how knowledge circulates within language models throughout the learning, tuning, and application processes, which may prevent us from further understanding the connections between current progress or realizing existing limitations. In this survey, we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods, and investigating how knowledge circulates when it is built, maintained and used. To this end, we systematically review existing studies of each period of the knowledge life cycle, summarize the main challenges and current limitations, and discuss future directions.Comment: paperlist: https://github.com/c-box/KnowledgeLifecycl

Automatic Document Summarization Using Knowledge Based System

This dissertation describes a knowledge-based system to create abstractive summaries of documents by generalizing new concepts, detecting main topics and creating new sentences. The proposed system is built on the Cyc development platform that consists of the world’s largest knowledge base and one of the most powerful inference engines. The system is unsupervised and domain independent. Its domain knowledge is provided by the comprehensive ontology of common sense knowledge contained in the Cyc knowledge base. The system described in this dissertation generates coherent and topically related new sentences as a summary for a given document. It uses syntactic structure and semantic features of the given documents to fuse information. It makes use of the knowledge base as a source of domain knowledge. Furthermore, it uses the reasoning engine to generalize novel information. The proposed system consists of three main parts: knowledge acquisition, knowledge discovery, and knowledge representation. Knowledge acquisition derives syntactic structure of each sentence in the document and maps words and their syntactic relationships into Cyc knowledge base. Knowledge discovery abstracts novel concepts, not explicitly mentioned in the document by exploring the ontology of mapped concepts and derives main topics described in the document by clustering the concepts. Knowledge representation creates new English sentences to summarize main concepts and their relationships. The syntactic structure of the newly created sentences is extended beyond simple subject-predicate-object triplets by incorporating adjective and adverb modifiers. This structure allows the system to create sentences that are more complex. The proposed system was implemented and tested. Test results show that the system is capable of creating new sentences that include abstracted concepts not mentioned in the original document and is capable of combining information from different parts of the document text to compose a summary

EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion

Commonsense knowledge graph completion is a new challenge for commonsense knowledge graph construction and application. In contrast to factual knowledge graphs such as Freebase and YAGO, commonsense knowledge graphs (CSKGs; e.g., ConceptNet) utilize free-form text to represent named entities, short phrases, and events as their nodes. Such a loose structure results in large and sparse CSKGs, which makes the semantic understanding of these nodes more critical for learning rich commonsense knowledge graph embedding. While current methods leverage semantic similarities to increase the graph density, the semantic plausibility of the nodes and their relations are under-explored. Previous works adopt conceptual abstraction to improve the consistency of modeling (event) plausibility, but they are not scalable enough and still suffer from data sparsity. In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility. Each node in CSKG finds its top entailed nodes using a finetuned transformer over natural language inference (NLI) tasks, which sufficiently capture textual entailment signals. The entailment relation between these nodes are further utilized to: 1) build new connections between source triplets and entailed nodes to densify the sparse CSKGs; 2) enrich the generalization ability of node representations by comparing the node embeddings with a contrastive loss. Experiments on two standard CSKGs demonstrate that our proposed framework EntailE can improve the performance of CSKG completion tasks under both transductive and inductive settings.Comment: 10 pages, 5 figures, 9 table

深層学習に基づく感情会話分析に関する研究

Owning the capability to express specific emotions by a chatbot during a conversation is one of the key parts of artificial intelligence, which has an intuitive and quantifiable impact on the improvement of chatbot’s usability and user satisfaction. Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. Recently, many studies on neural emotional conversational models have been conducted. However, enabling the chatbot to control what kind of emotion to respond to upon its own characters in conversation is still underexplored. At this stage, people are no longer satisfied with using a dialogue system to solve specific tasks, and are more eager to achieve spiritual communication. In the chat process, if the robot can perceive the user's emotions and can accurately process them, it can greatly enrich the content of the dialogue and make the user empathize. In the process of emotional dialogue, our ultimate goal is to make the machine understand human emotions and give matching responses. Based on these two points, this thesis explores and in-depth emotion recognition in conversation task and emotional dialogue generation task. In the past few years, although considerable progress has been made in emotional research in dialogue, there are still some difficulties and challenges due to the complex nature of human emotions. The key contributions in this thesis are summarized as below: (1) Researchers have paid more attention to enhancing natural language models with knowledge graphs these days, since knowledge graph has gained a lot of systematic knowledge. A large number of studies had shown that the introduction of external commonsense knowledge is very helpful to improve the characteristic information. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. In this work, we employ an external knowledge graph ATOMIC to extract the knowledge sources. We proposed KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. The conversation is a sequence of coherent and orderly discourses. For neural networks, the capture of long-range context information is a weakness. We adopt Transformer a structure composed of self-attention and feed forward neural network, instead of the traditional RNN model, aiming at capturing remote context information. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets. (2) We proposed an emotional dialogue model based on Seq2Seq, which is improved from three aspects: model input, encoder structure, and decoder structure, so that the model can generate responses with rich emotions, diversity, and context. In terms of model input, emotional information and location information are added based on word vectors. In terms of the encoder, the proposed model first encodes the current input and sentence sentiment to generate a semantic vector, and additionally encodes the context and sentence sentiment to generate a context vector, adding contextual information while ensuring the independence of the current input. On the decoder side, attention is used to calculate the weights of the two semantic vectors separately and then decode, to fully integrate the local emotional semantic information and the global emotional semantic information. We used seven objective evaluation indicators to evaluate the model's generation results, context similarity, response diversity, and emotional response. Experimental results show that the model can generate diverse responses with rich sentiment, contextual associations