2010 2nd International Conference on Computer Technology and Development (ICCTD 2010)

Conversational Ontology Construction based on Human Emotions

Vibhav Jain

Computer Science Department Jaypee Institute of Information Technology

Noida, India e-mail: jnvibhav@gmail.com

Abstract- In this paper we describe a solution for incorporating human emotions into the conversational system for automatic ontology construction. We find semantic similarity using LSA, Latent Semantic Analysis, between the new sentence and the corpus corresponding to each human emotion stored in ontology, structured database of sentences corresponding to human emotions. If the semantic similarity of the new sentence and the corpus has a highest value then new sentence will be inserted as a child of that corpus on the ontology and the emotion of the new sentence is the emotion of that corpus but the degree of the emotion depends on the percentage of the semantic value. We analyzed the 100 sentences and we got the estimation accuracy of about 75%.

Keywords-Human emotion; Latent Semantic Analysis; Automated Ontology Construction.

I. INTRODUCTION

When using ontology-based techniques for conversational system it is important for the ontology to capture the human emotions in a proper way. Ontology to be used for practical NLP is constructed for a specific situation. A definition of a situated ontology is given by Mahesh &

Nirenburg [1]: A situated ontology is as a world model used as a computational resource for solving a particular set of problems. They consider ontology as a database with information about what categories (or concepts) exist in the world/domain, what properties they have, and how they are related to one another. Ontology represents the concepts present in a given domain. Thus, ontology provides a common vocabulary that can be used to state facts and formulate questions about the domain.

Noy & Heffner [2] give examples of four different purposes for ontology creation, natural language application, theoretical investigation, knowledge sharing and reuse, and simulation and modeling.

To organize world knowledge is the general function of Ontologies and it is also the primary function of ontologies. A secondary function of ontologies is to organize the semantics of natural language expression i.e. source text. At the lowest level of ontology learning one typically has to deal with the task of extracting lexical entries referring to concepts and relations. Lexical acquisition deals with the task of acquiring syntactic and semantic classification of unknown words. A comprehensive overview on lexical acquisition in the context of information extraction is given in Basili and Pazienza [7] identify the following methodological areas for lexical acquisition: Statistical

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induction using collocation, syntactic features or lexemes; Logical induction using symbolic representations at word, phrase or sentence level; Quantitative machine learning referring to all other inductive methods that are not purely statistical (e.g. neural networks). A straightforward technique for extracting relevant lexical entries that may indicate concepts is just counting frequencies of lexical entries in a given set of documents. In general this approach is based on assumption that a frequent lexical entry in a set of domain specific texts indicates a surface of concepts. B10mqvist [8] give two different views of patterns. One view considers pattern mining and recognition where the aim is to find regularities in some set of objects. The other view is to use a predefined pattern, encoded best practices, as a template for constructing a new solution. Fortuna [9] introduces a system called Ontogen for semi automated construction of topic ontologies. Topic ontology consists of a set of topics (or concepts) and a set of relations between topics which best describes the data.

Guarino [10] distinguish four kinds of ontologies: Top­level ontologies include general concepts like time and space, objects, events etc. It is domain independent and therefore applicable for all problems and applications; Domain and Task ontologies, which captures, respectively a generic domain or a generic task; Application ontologies are both domain and task specific. These can be constructed through a set of domain and task ontologies related to application. Emotion is one of the most controversial topics in psychology, a source of intense discussion and disagreement from the earliest philosophers and other thinkers to the present day. Emotion faces, emotion elicitors and emotion neural process are the components of emotions. In general, there are at least three ways we can think of emotion.

1) Emotion as a state. This is the most common popular notion of emotion. For example, a person is angry means a person is in the angry state.

2) Emotion as a process [11). Emotion is considered as a complex dynamic interaction between cognition, physiology, and social elements because the "state" is constantly changing. Thus, emotion is really more about the changing interactions and less about any particular moment in time.

3) Emotion as an (indirect) knowledge source. Emotion provides additional information that can be used to make decisions and aid learning. It is indirect because, unlike many other sources of knowledge, the person does not query it for information.

2010 2nd International Conference on Computer Technology and Development (ICCTD 2010)

Human emotions are complex. They express positive or negative reactions to external and internal stimuli. Emotions categories on the existence of two types of human emotions: Primary emotions: whatever we feel first as a first response to a situation, e.g. when we hear of birthday party we may feel happiness, and Secondary emotions appear after the primary emotions and it is a mixture of emotions, e.g. if the power is cut down in birthday party we may feel sadness and anger.

For tagging positive and negative human emotion in the sentence we make an array of synonym of emotions. Table 1 shows some positive and negative emotions. For instance cheer, delight, enjoy, laugh are the synonym of happiness; broken heart, grief, depression are the synonym of sadness. We take 600 initial sentences corresponding to human emotion ej having degree of emotion dj and for testing we take 100 sentences. We got the accuracy of 75%.

Table 1: Types of Human Emotion Positive emotions Negative emotions

Love Fear

Happiness Anger

Hope Depression

Confidence Guilt

......... .........

When two person talking with each other before giving the response they first understand the emotion of other. So understanding of emotion is crucial for conversation. Therefore in human computer interaction computer must understand the emotion of a human after that generate a response to user. In this paper we estimate the emotion which helps the conversational agent to recognize emotion based on human language and behave more like us.

I I. ONTOLOGY CONSTRUCTION

Ontology is a structured database of idioms and initial sentences corresponding to human emotions having high degree of freedom. We are talking about automated ontology construction therefore we store some idioms and initial sentences for each human emotion as well as nouns corresponding to initial sentences. Human emotion of the sentence with its degree is the concept of the ontology. The size of world knowledge increases exponentially. We find the semantic similarity between new sentences with the sentences stored in the ontology. If the semantic value of the new sentence is greatest with one of the ontology sentence then this ontology sentence will create a new node for the new sentence and the emotion of that ontology sentence will become the emotion of the human sentence but the degree of emotion depend on the percentage of value we get.

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III. METHOD

The flow diagram of the total process shown on fig 1. It divides the whole process into three phases: sentences preprocessing, finding semantic similarity and insert the new sentence in the world knowledge as well as find the emotion.

A. Sentence Preprocessing

Sentence is a group of words. Our work is to find out the nouns from sentence so we pass it to Part-of-speech (POS) tagger. We utilize this Part-of-Speech (POS) tagged to find out the nouns.

B. Finding Semantic Similarity

After getting the nouns from the new sentence and stored sentences we construct the keyword-document matrix X

with K *D where K is the number of keywords and D is the number of documents. Element in the matrix is the number of occurrences of keyword in the documents. After that we apply SVD [6], singular value decomposition, operation on Keyword-document matrix. The SVD can extract knowledge of the document and transfer it into the semantic space. The SVD operation decomposes the keyword­document matrix into three matrices X=USV' and we choose top k values from singular matrix S [6] to reduce the dimension and rebuild matrix using matrix multiplication to get a LSA [6] matrix. LSA, Latent Semantic Analysis, is used to find out the latent information between the documents. Correlation is used to calculate the semantic similarity between two columns from LSA matrix. Equation of correlation is shown below where R is the correlation coefficient; n is the number of rows in LSA matrix; SSxx is the standard deviation of column x; SSyy is the standard deviation of column y and SSxy is the combined standard deviation of column x and y using multiplication.

C. Insert the Sentence in the Ontology

(I)

(2)

(3)

When semantic similarity value between new sentence and one of the ontology sentences is greatest then that ontology sentence will create a new node and the emotion of that ontology sentence will become the emotion of the new sentence but degree of the emotion is depend on the percentage of the similarity value. We may use table2 to estimate the degree of emotion.

IV. PRELIMIARYRESULT

For testing the above method we take some initial sentences are:

2010 2nd International Conference on Computer Technology and Development (ICCTD 2010)

New sentence Part-of-speech -. Tagger

� Singular Value

� Construct keyword-

Decomposition document matrix

� Select top k singular Reconstruct the Matrix

values � (LSA)

� Find Semantic similarity

and generate emotion.

Fig. 1: Finding semantic similarity between two utterances.

Dl: We are hoping for good weather on Sunday (Hope, very high).

D2: I am always very happy to see your happy face (Happiness, very high).

D3: Jim was so angry that john was afraid (angry, very high).

D4: We lived in fear of losing our jobs (fear, very high). D5: I will do my best to make you happy ever single day

(happiness, high). Table 2: Estimate degree of emotion

Similarity value Del!;ree of emotion Value <=0 .2 Very low 0.2< value <=0 .4 Low 0.4< value<= 0.6 Medium 0.6< value <= 0.8 High 0.8 < value <-I Very high

Now we take a new sentence D6: I have wished to have a happy family. This sentence has two emotional words wished and happy. So it's a mixed emotional sentence. We discovered that semantic similarity value of D6 with D2 is 0.99. Therefore the emotion of the sentence D6 is happiness and from table 2 we get degree of emotion i.e. very high. Therefore emotion and degree of emotion of sentence D6 is (happiness, very high) and D2 will create a new node for D6.

Now change the sentence D7: I have wished to have a happy family with no angry member. We have discovered that semantic similarity value of D7 with Dl is 0.73, with D2 is less than 0.3 and with D3 is 0.4. It happens because here the intention of the user has changed. He tells this sentence because he knows there are some members in his family have high anger and he hopes for a happy family. Therefore emotion and the degree of emotion of sentence D7 is (hope, high).

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V. CONCLUSION

Here, we try to propose a method of finding the degree of human emotion and adding the new sentence on the automated ontology. We use a novel method i.e. LSA to find out the semantic similarity between new sentence with the ontology sentences. If the value is highest with one of the ontology sentence then that ontology sentence will create a node for new sentence and the emotion of that ontology sentence will become the emotion of new sentence but the degree of emotion depends on the semantic value. In the future, we plan to make an algorithm to predict the behavior of a person using human computer interaction.

REFERENCES

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