Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1551-6709.2010.01106.x

Composition in distributional models of semantics.

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

In generating a suitable fuzzy classifier system, significant effort is often placed on the determination and the fine tuning of the fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within the fuzzy rules. Often traditional fuzzy inference strategies are used which consequently provide no control over how strongly or weakly the inference is applied within these rules. Furthermore such strategies will allow no interaction between grades of membership. A number of theoretical fuzzy inference operators have been proposed for both regression and classification problems but they have not been investigated in the context of real-world applications. In this paper we propose a novel genetic algorithm framework for optimizing the strength of fuzzy inference operators concurrently with the tuning of membership functions for a given fuzzy classifier system. Each fuzzy system is generated using two well-established decision tree algorithms: C4.5 and CHAID. This will enable both classification and regression problems to be addressed within the framework. Each solution generated by the genetic algorithm will produce a set of fuzzy membership functions and also determine how strongly the inference will be applied within each fuzzy rule. We investigate several theoretical proven fuzzy inference techniques (T-norms) in the context of both classification and regression problems. The methodology proposed is applied to a number of real-world data sets in order to determine the effects of the simultaneous tuning of membership functions and inference parameters on the accuracy and robustness of fuzzy classifiers.

Genetic tuning of fuzzy inference within fuzzy classifier systems

This paper extends a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. Initial experiments on classifying questions in the presence of a mix of straightforward and "difficult" non-questions yielded promising results, with classification accuracy approaching 90%. However, this initial dataset does not fully represent the various permutations of natural language in which sentences may occur. Also, a higher Classification Accuracy is desirable for real-world applications. Following an analysis of categorisation of sentences, we present a series of experiments that show improved performance over the initial experiment and promising performance for categorising more complex combinations in the future.

/pdf/a-multi-classifier-approach-to-dialogue-act-classification-akq1zj1gfp.pdf

A multi-classifier approach to dialogue act classification using function words

This research presents a new benchmark dataset for evaluating Short Text Semantic Similarity (STSS) measurement algorithms and the methodology used for its creation. The power of the dataset is evaluated by using it to compare two established algorithms, STASIS and Latent Semantic Analysis. This dataset focuses on measures for use in Conversational Agents; other potential applications include email processing and data mining of social networks. Such applications involve integrating the STSS algorithm in a complex system, but STSS algorithms must be evaluated in their own right and compared with others for their effectiveness before systems integration. Semantic similarity is an artifact of human perception; therefore its evaluation is inherently empirical and requires benchmark datasets derived from human similarity ratings. The new dataset of 64 sentence pairs, STSS-131, has been designed to meet these requirements drawing on a range of resources from traditional grammar to cognitive neuroscience. The human ratings are obtained from a set of trials using new and improved experimental methods, with validated measures and statistics. The results illustrate the increased challenge and the potential longevity of the STSS-131 dataset as the Gold Standard for future STSS algorithm evaluation.

/pdf/a-new-benchmark-dataset-with-production-methodology-for-51obgou68j.pdf

A new benchmark dataset with production methodology for short text semantic similarity algorithms

Short text semantic similarity measurement is a new and rapidly growing field of research. 'Short texts' are typically sentence length but are not required to be grammatically correct. There is great potential for applying these measures in fields such as information retrieval, dialogue management and question answering. A dataset of 65 sentence pairs, with similarity ratings, produced in 2006 has become adopted as a de facto gold standard benchmark. This paper discusses the adoption of the 2006 dataset, lays down a number of criteria that can be used to determine whether a dataset should be awarded a 'gold standard' accolade and illustrates its use as a benchmark. Procedures for the generation of further gold standard datasets in this field are recommended.

/pdf/benchmarking-short-text-semantic-similarity-9fn2h8wd0q.pdf

Benchmarking short text semantic similarity

This paper focuses on the implementation of a novel semantic-based Conversational Agent (CA) framework. Traditional CAs interpret scripts consisting of structural patterns of sentences. User input is matched against such patterns and an associated response is sent as output. This traditional CA approach, which solely takes into account the structural form of a sentence, requires the scripter to anticipate the inordinate ways that a user may send input. This is a tiresome and timeconsuming process. As such, a semantic-based CA that interprets scripts consisting of natural language sentences alleviates this burden by removing the process of pattern generation. The CA was evaluated by participants using a domain of a specific nature, that is, student debt management, which indicated promising results.

/pdf/a-conversational-agent-framework-using-semantic-analysis-2uev831cxh.pdf

Zuhair Bandar

Papers

Genetic tuning of fuzzy inference within fuzzy classifier systems

A multi-classifier approach to dialogue act classification using function words

A new benchmark dataset with production methodology for short text semantic similarity algorithms

Benchmarking short text semantic similarity

A Conversational Agent Framework using Semantic Analysis