An unsupervised topic segmentation model incorporating word order

Jameel, Shoaib; Lam, Wai

doi:10.1145/2484028.2484062

Cited by 54 publications

(35 citation statements)

References 37 publications

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“…The approaches cannot distinguish between "Daniele loves Victoria" and "Victoria loves Daniele", but only represent the meaning as pertaining to love. However, there is research attempting to address this problem by incorporating word order into the model (e.g., see Jameel & Lam, 2013).…”

Section: Evaluation Of Word Count Strategies and Statistical Semanticsmentioning

confidence: 99%

Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating Scales

Kjell¹

2018

Preprint

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How to define and measure individuals’ well-being is important, as this has an impact on both research and society at large. This thesis concerns how to define and measure the self-reported well-being of individuals, which involves both theorizing as well as developing and applying empirical and statistical methods in order to gain a better understanding of well-being.The first paper critically reviews the literature on well-being. It identifies an individualistic bias in current approaches and accompanying measures related to well-being and happiness; for example, through an over-emphasis on the importance of self-centered aspects of well-being (e.g., the unprecedented focus on satisfaction with life) whilst disregarding the importance of harmony in life, interconnectedness and psychological balance in relation to well- being. It is also discussed how closed-ended well-being measures impose the researchers’ values and limit the ability of respondents to express themselves in regard to their perceived well-being.The second paper addresses concerns regarding this individualistic bias by developing the harmony in life scale, which focuses on interconnectedness and psychological balance. In addition, an open-ended approach is developed in the paper, allowing individuals to freely describe their pursuit of well-being by means of open-ended responses analyzed using statistical semantics (including techniques from artificial intelligence such as natural language processing and machine learning). The results show that the harmony in life scale and the traditional satisfaction with life scale form a two-factor model of well-being, where the harmony in life scale explains more unique variance in measures of psychological well-being, stress, depression and anxiety, but not happiness. It is further demonstrated that participants describe their pursuit of harmony in life using words related to interconnectedness (including words such as: peace, balance, cooperation), whereas they describe their pursuit of satisfaction with life using words related to independence (including words such as: money, achievement, fulfillment). It is concluded that the harmony in life scale complements the satisfaction with life scale for a more comprehensive understanding of subjective well-being.The third paper focuses on developing and evaluating a method for measuring and describing psychological constructs using open-ended questions analyzed by means of statistical semantics rather than closed-ended numerical rating scales. This semantic measures approach is tested and compared with traditional rating scales in nine studies, including two different paradigms involving reports regarding objective stimuli (i.e., the evaluation of facial expressions) and reports regarding subjective states (i.e., the self-reporting of harmony in life, satisfaction with life, depression and worry). The results indicate that semantic measures encompass higher, or competitive, levels of reliability and validity compared to traditional numerical rating scales. In addition, semantic measures appear to be better suited for differentiating between psychological constructs, such as harmony in life versus satisfaction with life as well as depression versus worry.In this thesis, the findings from these three papers are elaborated and integrated into two independent perspectives. The first perspective focuses on the theoretical and empirical differences between harmony in life and satisfaction with life within a context of societal and national progress. It is concluded that harmony in life complements satisfaction with life. The second perspective focuses on the open-ended, statistical semantics approach. It is proposed that statistical semantics may beneficially be used more widely as a research tool within psychological research.

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Section: Evaluation Of Word Count Strategies and Statistical Semanticsmentioning

confidence: 99%

Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating Scales

Kjell¹

2018

Preprint

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“…Performance of this method when dealing with large unstructured text data was not satisfactory and consumed significant amount of time to construct the matrix. A new model that performs phrase segmentation along with topic modeling was introduced [8] but failed to work with datasets having large number of files. TopMine [9], a system capable of mining topical phrase mining, is introduced recently, which implements a two step process for discovering phrases from text and for training a tradition topic model such as LDA.…”

Section: State Of the Art In Concept Extractionmentioning

confidence: 99%

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

Anoop¹,

Asharaf²

2017

IJIMAI

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The task of mining large unstructured text archives, extracting useful patterns and then organizing them into a knowledgebase has attained a great attention due to its vast array of immediate applications in business. Businesses thus demand new and efficient algorithms for leveraging potentially useful patterns from heterogeneous data sources that produce huge volumes of unstructured data. Due to the ability to bring out hidden themes from large text repositories, topic modeling algorithms attained significant attention in the recent past. This paper proposes an efficient and scalable method which is guided by topic modeling for extracting concepts and relationships from e-commerce product descriptions and organizing them into knowledgebase. Semantic graphs can be generated from such a knowledgebase on which meaning aware product discovery experience can be built for potential buyers. Extensive experiments using proposed unsupervised algorithms with e-commerce product descriptions collected from open web shows that our proposed method outperforms some of the existing methods of leveraging concepts and relationships so that efficient knowledgebase construction is possible.

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“…For example, the topical n-gram model (TNG) introduced by Wang et al (2007) models unigram and n-gram phrases as mixture of topics based on the nearby word context. More recently, Jameel & Lam (2013) proposed an LDA extension that uses word sequence information to generate topic distribution over n-grams and performs topic segmentation using segment and paragraph information. While these and many other approaches offer a better and more realistic modeling of word sequences, they don't model topical variations across document sections either in mono-or multilingual collections.…”

Section: Introductionmentioning

confidence: 99%

Online Multilingual Topic Models with Multi-Level Hyperpriors

Krstovski

Smith

Kurtz

2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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For topic models, such as LDA, that use a bag-of-words assumption, it becomes especially important to break the corpus into appropriately-sized "documents". Since the models are estimated solely from the term cooccurrences, extensive documents such as books or long journal articles lead to diffuse statistics, and short documents such as forum posts or product reviews can lead to sparsity. This paper describes practical inference procedures for hierarchical models that smooth topic estimates for smaller sections with hyperpriors over larger documents. Importantly for large collections, these online variational Bayes inference methods perform a single pass over a corpus and achieve better perplexity than "flat" topic models on monolingual and multilingual data. Furthermore, on the task of detecting document translation pairs in large multilingual collections, polylingual topic models (PLTM) with multi-level hyperpriors (mlhPLTM) achieve significantly better performance than existing online PLTM models while retaining computational efficiency.

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An unsupervised topic segmentation model incorporating word order

Cited by 54 publications

References 37 publications

Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating Scales

Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating Scales

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

Online Multilingual Topic Models with Multi-Level Hyperpriors

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