Data Mining Practical Machine Learning Tools and Techniques

The measurement of meaning

Sentiment analysis algorithms and applications: A survey

コンピュータ・サイエンス : ACM computing surveys

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

Temporal information retrieval has been a topic of great interest in recent years Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries In this article, we present a survey of the existing literature on temporal information retrieval In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remains undeveloped

/pdf/survey-of-temporal-information-retrieval-and-related-e5b5kvs380.pdf

Survey of Temporal Information Retrieval and Related Applications

The availability of contiguous and non-contiguous multiword lexical units (MWUs) in Natural Language Processing (NLP) lexica enhances parsing precision, helps attachment decisions, improves indexing in information retrieval (IR) systems, reinforces information extraction (IE) and text mining, among other applications. Unfortunately, their acquisition has long been a significant problem in NLP, IR and IE. In this paper we propose two new association measures, the Symmetric Conditional Probability (SCP) and the Mutual Expectation (ME) for the extraction of contiguous and non-contiguous MWUs. Both measures are used by a new algorithm, the LocalMaxs, that requires neither empirically obtained thresholds nor complex linguistic filters. We assess the results obtained by both measures by comparing them with reference association measures (Specific Mutual Information, o 2 , Dice and Log-Likelihood coefficients) over a multilingual parallel corpus. An additional experiment has been carried out over a part-of-speech tagged Portuguese corpus for extracting contiguous compound verbs.

Using LocalMaxs algorithm for the extraction of contiguous and non-contiguous multiword lexical units

The availability of contiguous and non-contiguous multiword lexical units (MWUs) in Natural Language Processing (NLP) lexica enhances parsing precision, helps attachment decisions, improves indexing in information retrieval (IR) systems, reinforces information extraction (IE) and text mining, among other applications. Unfortunately, their acquisition has long been a significant problem in NLP, IR and IE. In this paper we propose two new association measures, the Symmetric Conditional Probability (SCP) and the Mutual Expectation (ME) for the extraction of contiguous and noncontiguous MWUs. Both measures are used by a new algorithm, the LocalMaxs, that requires neither empirically obtained thresholds nor complex linguistic filters. We assess the results obtained by both measures by comparing them with reference association measures (Specific Mutual Information, Φ2, Dice and Log-Likelihood coefficients) over a multilingual parallel corpus. An additional experiment has been carried out over a part-of-speech tagged Portuguese corpus for extracting contiguous compound verbs.

/pdf/using-localmaxs-algorithm-for-the-extraction-of-contiguous-kb5g9nyhqz.pdf

Using LocalMaxs Algorithm for the Extraction of Contiguous and Non-contiguous Multiword Lexical Units

We propose a novel multitask learning attention -based deep neural network model, which facilitates the fusion of various modalities. In particular, we use this network to both regress and classify the level of depression. Acoustic, textual, and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus -a Wizard of Oz. From the results, we empirically justify that a) multitask learning networks cotrained over regression and classification have better performance compared to single -task networks, and b) the fusion of all the modalities helps in giving the most accurate estimation of depression with respect to regression.

Multitask Representation Learning for Multimodal Estimation of Depression Level

In order to solve problems of reliability of systems based on lexical repetition and problems of adaptability of language-dependent systems, we present a context-based topic segmentation system based on a new informative similarity measure based on word co-occurrence. In particular, our evaluation with the state-of-the-art in the domain i.e. the c99 and the TextTiling algorithms shows improved results both with and without the identification of multiword units.

/pdf/topic-segmentation-algorithms-for-text-summarization-and-56x4rtiqo3.pdf

Gaël Dias

Papers

Survey of Temporal Information Retrieval and Related Applications

Using LocalMaxs algorithm for the extraction of contiguous and non-contiguous multiword lexical units

Using LocalMaxs Algorithm for the Extraction of Contiguous and Non-contiguous Multiword Lexical Units

Multitask Representation Learning for Multimodal Estimation of Depression Level

Topic segmentation algorithms for text summarization and passage retrieval: an exhaustive evaluation