DOCSLIB.ORG

An Unsupervised Topic Segmentation Model Incorporating Word Order

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Unsupervised Topic Segmentation Model Incorporating Word Order An Unsupervised Topic Segmentation Model Incorporating Word Order Shoaib Jameel and Wai Lam The Chinese University of Hong Kong One line summary of the work We will see how maintaining the document structure such as paragraphs, sentences, and the word order helps improve the performance of a topic model. Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 1 Outline Motivation Related Work I Probabilistic Unigram Topic Model (LDA) I Probabilistic N-gram Topic Models I Topic Segmentation Models Overview of our model I Our N-gram Topic Segmentation model (NTSeg) Text Mining Experiments of NTSeg I Word-Topic and Segment-Topic Correlation Graph I Topic Segmentation Experiment I Document Classification Experiment I Document Likelihood Experiment Conclusions and Future Directions Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 2 Motivation Many works in the topic modeling literature assume exchangeability among the words. As a result we see many ambiguous words in topics. For example, consider few topics obtained from the NIPS collection using the Latent Dirichlet Allocation (LDA) model: Example Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 architecture order connectionist potential prior recurrent first role membrane bayesian network second binding current data module analysis structures synaptic evidence modules small distributed dendritic experts The problem with the LDA model Words in topics are not insightful. Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 3 Motivation Many works in the topic modeling literature assume exchangeability among the words. As a result we see many ambiguous words in topics. For example, consider few topics obtained from the NIPS collection using the Latent Dirichlet Allocation (LDA) model: Example Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 architecture order connectionist potential prior recurrent first role membrane bayesian network second binding current data module analysis structures synaptic evidence modules small distributed dendritic experts The problem with the LDA model Words in topics are not insightful. Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 3 Latent Dirichlet Allocation Model (LDA) (Blei et al., JMLR-2003) Generative Process Graphical Model in Plate 1 Draw θ from Dirichlet(α), Diagram where each θ(d) consists of topic distribution for document θ α d 2 Draw φ from Dirichlet(β), where φ encompasses word distribution for topic z β 3 For every word in the document d (d) 1 Draw a topic zi from Multinomial (θ(d)) (d) w φ 2 Draw a word wi from Multinomial (φ (d) ) N Z zi M Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 4 Relaxing the Bag-of-Words Assumption in a Topic Model Can the bag-of-words assumption be relaxed in a topic model? This makes more sense as this is how documents are written by humans and also read. more Here things are lot more dog a organized. I can understand noticed that it was actually the cat I which noticed a dog. lot cat understand don’t things here Figure : This is how the bag-of-words looks (left) - complete chaos. The one on the right makes more sense to us. Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 5 Relaxing the Bag-of-Words Assumption Bigram Topic Model (BTM) (Wallach, ICML-2006) Some Properties of the model Graphical Model of BTM Word is generated by both the α topic and the previous word θ Inspired by the Hierarchical Dirichlet Language Model (d) (d) (d) (d) zi 1 zi zi+1 zi+2 Better empirical results than − the LDA model (d) (d) (d) (d) w A limitation of the model wi 1 wi wi+1 i+2 − I Always generates bigrams in M a topic σ δ ZV Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 6 Relaxing the Bag-of-Words Assumption LDA-Collocation Model (LDACOL) (Griffiths et al., Psy. Rev-2007) Some Properties of the model Graphical Model of LDACOL Word is generated by the α topic, the previous word and a binary bigram status variable θ Each word has a topic (d) (d) (d) (d) ψ assignment and a collocation zi 1 zi zi+1 zi+2 − V assignment (d) (d) (d) xi xi+1 xi+2 Can form longer order phrases γ (d) (d) (d) (d) wi 1 wi wi+1 wi+2 − Can generate both unigrams M and bigram words β φ σ δ A limitation of the model Z V I Only the first word in a bigram has a topic assignment Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 7 Relaxing the Bag-of-Words Assumption Topical N-Gram Model (TNG) (Wang et al., ICDM-2007) Some Properties of the model Graphical Model of TNG Extends the LDACOL model α Each word has a topic assignment and a collocation θ assignment (d) (d) (d) (d) ψ zi 1 zi zi+1 zi+2 Can form longer order phrases − ZV (d) (d) (d) Can generate both unigrams xi xi+1 xi+2 γ and bigram words (d) (d) (d) (d) wi 1 wi wi+1 wi+2 − A limitation of the model M I Words in a bigram may have β φ σ δ different topic assignments Z ZV Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 8 What Topic N-gram models do - An Illustration Abstract We give necessary and sufficient conditions for uniqueness of the support vector solution for the problems of 1 Para. pattern recognition and regression estimation, for a general class of cost functions. We sho that if the solution is not unique, all support vectors are necessarily at bound, and we give some simple examples of non-unique solutions. We note that uniqueness of the primal (dual) silution does not necessarily imply uniqueness of the dual (primal) solution. We show how to compute the threshold b when the solution is unique, but when all support vectors are bound, in which ... aa 2 Para. Acknowledgements C. Burges wishes to thank W. Keasler, V. Lawrence and C. Nohl of Lucent Technologies for their case the usual method for determining b does not work... support. Reference [1] R. Fletcher, Practical Methods of Optimization. John Wiley and Sons, Inc., 2nd edition, 1987. Topic 1 Topic 2 support vector acknowledgements cost functions reference Consider the document as a whole Find topical n-grams in the document Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 9 Bag of Words in Topic Segmentation These models maintain the document structure such as paragraphs or sentences Assume that words within a paragraph or a sentence are exchangeable Introduces the notion of segment-topics or super-topics and word-topics Paragraph n in the document d follow 1 this is will next 2 paragraph follow this is 3 paragraph next 2 will Paragraph n +1 in the document d Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 10 A Topic Segmentation Model (LDSEG) (Shafiei et al., Canadian AI-2008) Model Properties Graphical Model in Plate Performs topic segmentation Diagram Can work at paragraph and τ ρ sentence level π c a binary variable gives the Ω y c change in topics segment-wise Segments come from a θ α predefined number of super-topics β The super-topics comprise of z a mixture of word-topics w φ N Z S M Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 11 A Topic Segmentation Model (LDSEG) (Shafiei et al., Canadian AI-2008) Model Properties Graphical Model in Plate This region is similar to the Diagram LDA model τ ρ Segments exhibit multiple π topics Ω y c Words are generated from a predefined number of word-topics θ α β z w φ N Z S M Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 12 Topic Segmentation Illustration using the LDSEG model Abstract We give necessary and sufficient conditions for uniqueness of the support vector solution for the problems of pattern recognition and regression estimation, for a general class of cost functions. We sho that if the solution is not 1 Para. unique, all support vectors are necessarily at bound, and we give some simple examples of non-unique solutions. We Super-Topic 7 note that uniqueness of the primal (dual) silution does not necessarily imply uniqueness of the dual (primal) solution. We show how to compute the threshold b when the solution is unique, but when all support vectors are bound, in which ... Acknowledgements C. Burges wishes to thank W. Keasler, V. Lawrence and C. Nohl of Lucent Technologies for their 2 Para. case the usual method for determining b does not work... Super-Topic 4 support. Reference [1] R. Fletcher, Practical Methods of Optimization. John Wiley and Sons, Inc., 2nd edition, 1987. Word-Topic 1 Word-Topic 2 Super-Topic 1 Super-Topic 2 support acknowledgements Word-Topic 1 Word-Topic 5 vector reference Word-Topic 9 Word-Topic 8 cost Word-Topic 12 Word-Topic 1 Performs topic segmentation Unigram words are assigned to the word-topics Segments are assigned to the document-topics or super-topics Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 13 Our Research Contributions We propose a model called, NTSeg Our proposed model maintains the document structure such as paragraphs and sentences Maintains the order of the words in the document Detects and coordinates two topic granularity levels I Segment-Topics I Word-Topics Derivation of the posterior inference scheme Conducted extensive text mining experiments I Shown improvement over state-of-the-art models Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 14 Our Proposed Model (NTSeg) Ω ρ α K π(d) τ (d) (d) (d) ys 1 ys − (d) cs 1 − (d) cs (d) (d) θs 1 θs − (d) (d) (d) z z z (d) (d) (d) s 1,n 1 s 1,n s 1,n+1 zs,n 1 zs,n zs,n+1 − − − − − (d) (d) x (d) (d) xs 1,n s 1,n+1 xs,n xs,n+1 − − (d) (d) (d) (d) (d) (d) ws 1,n 1 ws 1,n ws 1,n+1 w − − − ws,n 1 ws,n s,n+1 − − M β φ ψ γ σ δ Z ZV ZV Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 15 Our Proposed Model (NTSeg) Ω ρ α K π(d) τ (d) (d) (d) ys−1 ys (d) cs−1 (d) cs (d) (d) θs−1 θs (d) (d) (d) d (d) z − − z − z − (d) ( ) s 1,n 1 s 1,n s 1,n+1 zs,n−1
Load more

Recommended publications
  • Multi-View Learning for Hierarchical Topic Detection on Corpus of Documents
    Multi-view learning for hierarchical topic detection on corpus of documents Juan Camilo Calero Espinosa Universidad Nacional de Colombia Facultad de Ingenieria, Departamento de Ingenieria de Sistemas e Industrial. Bogot´a,Colombia 2021 Multi-view learning for hierarchical topic detection on corpus of documents Juan Camilo Calero Espinosa Tesis presentada como requisito parcial para optar al t´ıtulode: Magister en Ingenier´ıade Sistemas y Computaciøn´ Director: Ph.D. Luis Fernando Ni~noV. L´ıneade Investigaci´on: Procesamiento de lenguaje natural Grupo de Investigaci´on: Laboratorio de investigaci´onen sistemas inteligentes - LISI Universidad Nacional de Colombia Facultad de Ingenieria, Departamento de Ingenieria en Sistemas e Industrial. Bogot´a,Colombia 2021 To my parents Maria Helena and Jaime. To my aunts Patricia and Rosa. To my grandmothers Lilia and Santos. Acknowledgements To Camilo Alberto Pino, as the original thesis idea was his, and for his invaluable teaching of multi-view learning. To my thesis advisor, Luis Fernando Ni~no,and the Laboratorio de investigaci´onen sistemas inteligentes - LISI, for constantly allowing me to learn new knowl- edge, and for their valuable recommendations on the thesis. V Abstract Topic detection on a large corpus of documents requires a considerable amount of com- putational resources, and the number of topics increases the burden as well. However, even a large number of topics might not be as specific as desired, or simply the topic quality starts decreasing after a certain number. To overcome these obstacles, we propose a new method- ology for hierarchical topic detection, which uses multi-view clustering to link different topic models extracted from document named entities and part of speech tags.
    [Show full text]
  • Nonstationary Latent Dirichlet Allocation for Speech Recognition
    Nonstationary Latent Dirichlet Allocation for Speech Recognition Chuang-Hua Chueh and Jen-Tzung Chien Department of Computer Science and Information Engineering National Cheng Kung University, Tainan, Taiwan, ROC {chchueh,chien}@chien.csie.ncku.edu.tw model was presented for document representation with time Abstract evolution. Current parameters served as the prior information Latent Dirichlet allocation (LDA) has been successful for to estimate new parameters for next time period. Furthermore, document modeling. LDA extracts the latent topics across a continuous time dynamic topic model [12] was developed by documents. Words in a document are generated by the same incorporating a Brownian motion in the dynamic topic model, topic distribution. However, in real-world documents, the and so the continuous-time topic evolution was fulfilled. usage of words in different paragraphs is varied and Sparse variational inference was used to reduce the accompanied with different writing styles. This study extends computational complexity. In [6][7], LDA was merged with the LDA and copes with the variations of topic information the hidden Markov model (HMM) as the HMM-LDA model within a document. We build the nonstationary LDA (NLDA) where the Markov states were used to discover the syntactic by incorporating a Markov chain which is used to detect the structure of a document. Each word was generated either from stylistic segments in a document. Each segment corresponds to the topic or the syntactic state. The syntactic words and a particular style in composition of a document. This NLDA content words were modeled separately. can exploit the topic information between documents as well This study also considers the superiority of HMM in as the word variations within a document.
    [Show full text]
  • Large-Scale Hierarchical Topic Models
    Large-Scale Hierarchical Topic Models Jay Pujara Peter Skomoroch Department of Computer Science LinkedIn Corporation University of Maryland 2029 Stierlin Ct. College Park, MD 20742 Mountain View, CA 94043 [email protected] [email protected] Abstract In the past decade, a number of advances in topic modeling have produced sophis- ticated models that are capable of generating hierarchies of topics. One challenge for these models is scalability: they are incapable of working at the massive scale of millions of documents and hundreds of thousands of terms. We address this challenge with a technique that learns a hierarchy of topics by iteratively apply- ing topic models and processing subtrees of the hierarchy in parallel. This ap- proach has a number of scalability advantages compared to existing techniques, and shows promising results in experiments assessing runtime and human evalu- ations of quality. We detail extensions to this approach that may further improve hierarchical topic modeling for large-scale applications. 1 Motivation With massive datasets and corresponding computational resources readily available, the Big Data movement aims to provide deep insights into real-world data. Realizing this goal can require new approaches to well-studied problems. Complex models that, for example, incorporate many de- pendencies between parameters have alluring results for small datasets and single machines but are difficult to adapt to the Big Data paradigm. Topic models are an interesting example of this phenomenon. In the last decade, a number of sophis- ticated techniques have been developed to model collections of text, from Latent Dirichlet Allocation (LDA)[1] through extensions using statistical machinery such as the nested Chinese Restaurant Pro- cess [2][3] and Pachinko Allocation[4].
    [Show full text]
  • Journal of Applied Sciences Research
    Copyright © 2015, American-Eurasian Network for Scientific Information publisher JOURNAL OF APPLIED SCIENCES RESEARCH ISSN: 1819-544X EISSN: 1816-157X JOURNAL home page: http://www.aensiweb.com/JASR 2015 October; 11(19): pages 50-55. Published Online 10 November 2015. Research Article A Survey on Correlation between the Topic and Documents Based on the Pachinko Allocation Model 1Dr.C.Sundar and 2V.Sujitha 1Associate Professor, Department of Computer Science and Engineering, Christian College of Engineering and Technology, Dindigul, Tamilnadu-624619, India. 2PG Scholar, Department of Computer Science and Engineering, Christian College of Engineering and Technology, Dindigul, Tamilnadu-624619, India. Received: 23 September 2015; Accepted: 25 October 2015 © 2015 AENSI PUBLISHER All rights reserved ABSTRACT Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. In existing system, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is used.The patterns are generated from the words in the word-based topic representations of a traditional topic model such as the LDA model. This ensures that the patterns can well represent the topics because these patterns are comprised of the words which are extracted by LDA based on sample occurrence of the words in the documents. The Maximum matched pat-terns, which are the largest patterns in each equivalence class that exist in the received documents, are used to calculate the relevance words to represent topics. However, LDA does not capture correlations between topics and these not find the hidden topics in the document. To deal with the above problem the pachinko allocation model (PAM) is proposed.
    [Show full text]
  • Incorporating Domain Knowledge in Latent Topic Models
    INCORPORATING DOMAIN KNOWLEDGE IN LATENT TOPIC MODELS by David Michael Andrzejewski A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Sciences) at the UNIVERSITY OF WISCONSIN–MADISON 2010 c Copyright by David Michael Andrzejewski 2010 All Rights Reserved i For my parents and my Cho. ii ACKNOWLEDGMENTS Obviously none of this would have been possible without the diligent advising of Mark Craven and Xiaojin (Jerry) Zhu. Taking a bioinformatics class from Mark as an undergraduate initially got me excited about the power of statistical machine learning to extract insights from seemingly impenetrable datasets. Jerry’s enthusiasm for research and relentless pursuit of excellence were a great inspiration for me. On countless occasions, Mark and Jerry have selflessly donated their time and effort to help me develop better research skills and to improve the quality of my work. I would have been lucky to have even a single advisor as excellent as either Mark or Jerry; I have been extremely fortunate to have them both as co-advisors. My committee members have also been indispensable. Jude Shavlik has always brought an emphasis on clear communication and solid experimental technique which will hopefully stay with me for the rest of my career. Michael Newton helped me understand the modeling issues in this research from a statistical perspective. Working with prelim committee member Ben Liblit gave me the exciting opportunity to apply machine learning to a very challenging problem in the debugging work presented in Chapter 4. I also learned a lot about how other computer scientists think from meetings with Ben.
    [Show full text]
  • Extração De Informação Semântica De Conteúdo Da Web 2.0
    Mestrado em Engenharia Informática Dissertação Relatório Final Extração de Informação Semântica de Conteúdo da Web 2.0 Ana Rita Bento Carvalheira [email protected] Orientador: Paulo Jorge de Sousa Gomes [email protected] Data: 1 de Julho de 2014 Agradecimentos Gostaria de começar por agradecer ao Professor Paulo Gomes pelo profissionalismo e apoio incondicional, pela sincera amizade e a total disponibilidade demonstrada ao longo do ano. O seu apoio, não só foi determinante para a elaboração desta tese, como me motivou sempre a querer saber mais e ter vontade de fazer melhor. À minha Avó Maria e Avô Francisco, por sempre estarem presentes quando eu precisei, pelo carinho e afeto, bem como todo o esforço que fizeram para que nunca me faltasse nada. Espero um dia poder retribuir de alguma forma tudo aquilo que fizeram por mim. Aos meus Pais, pelos ensinamentos e valores transmitidos, por tudo o que me proporcionaram e por toda a disponibilidade e dedicação que, constantemente, me oferecem. Tudo aquilo que sou, devo-o a vocês. Ao David agradeço toda a ajuda e compreensão ao longo do ano, todo o carinho e apoio demonstrado em todas as minhas decisões e por sempre me ter encorajado a seguir os meus sonhos. Admiro-te sobretudo pela tua competência e humildade, pela transmissão de força e confiança que me dás em todos os momentos. Resumo A massiva proliferação de blogues e redes sociais fez com que o conteúdo gerado pelos utilizadores, presente em plataformas como o Twitter ou Facebook, se tornasse bastante valioso pela quantidade de informação passível de ser extraída e explorada.
    [Show full text]
  • A New Method and Application for Studying Political Text in Multiple Languages
    The Pennsylvania State University The Graduate School THE RADICAL RIGHT IN PARLIAMENT: A NEW METHOD AND APPLICATION FOR STUDYING POLITICAL TEXT IN MULTIPLE LANGUAGES A Dissertation in Political Science by Mitchell Goist Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2020 ii The dissertation of Mitchell Goist was reviewed and approved* by the following: Burt L. Monroe Liberal Arts Professor of Political Science Dissertation Advisor Chair of Committee Bruce Desmarais Associate Professor of Political Science Matt Golder Professor of Political Science Sarah Rajtmajer Assistant Professor of Information Science and Tecnology Glenn Palmer Professor of Political Science and Director of Graduate Studies iii ABSTRACT Since a new wave of radical right support in the early 1980s, scholars have sought to understand the motivations and programmatic appeals of far-right parties. However, due to their small size and dearth of data, existing methodological approaches were did not allow the direct study of these parties’ behavior in parliament. Using a collection of parliamentary speeches from the United Kingdom, Germany, Spain, Italy, the Netherlands, Finland, Sweden, and the Czech Re- public, Chapter 1 of this dissertation addresses this problem by developing a new model for the study of political text in multiple languages. Using this new method allows the construction of a shared issue space where each party is embedded regardless of the language spoken in the speech or the country of origin. Chapter 2 builds on this new method by explicating the ideolog- ical appeals of radical right parties. It finds that in some instances radical right parties behave similarly to mainstream, center-right parties, but distinguish themselves by a focus on individual crime and an emphasis on negative rhetorical frames.
    [Show full text]
  • Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
    The paper has been accepted at Engineering Applications of Artificial Intelligence on 9 May 2017. This paper can cite as: Monika Rani, Amit Kumar Dhar, O.P. Vyas, Semi-automatic terminology ontology learning based on topic modeling, Engineering Applications of Artificial Intelligence, Volume 63, August 2017, Pages 108-125,ISSN 0952- 1976,https://doi.org/10.1016/j.engappai.2017.05.006. (http://www.sciencedirect.com/science/article/pii/S0952197617300891. Semi-Automatic Terminology Ontology Learning Based on Topic Modeling Monika Rani*, Amit Kumar Dhar and O. P. Vyas Department of Information Technology, Indian Institute of Information Technology, Allahabad, India [email protected] Abstract- Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques. In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user‟s query demonstrating the effectiveness of the proposed approach. Keywords: Ontology Learning (OL), Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD), Probabilistic Latent Semantic Indexing (pLSI), MapReduce Latent Dirichlet Allocation (Mr.LDA), Correlation Topic Modeling (CTM).
    [Show full text]
  • Song Lyric Topic Modeling
    A Comparison of Topic Modeling Approaches for a Comprehensive Corpus of Song Lyrics Alen Lukic Language Technologies Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 [email protected] Abstract In the interdisciplinary field of music information retrieval (MIR), applying topic modeling techniques to corpora of song lyrics remains an open area of research. In this paper, I discuss previous approaches to this problem and approaches which remain unexplored. I attempt to improve upon previous work by harvesting a larger, more comprehensive corpus of song lyrics. I will apply previously attempted topic modeling algorithms to this corpus, including latent Dirichlet allocation (LDA). In addition, I will also apply the Pachinko allocation (PAM) tech- nique to this corpus. PAM is a directed acylic graph-based topic modeling algorithm which models correlations between topics as well as words and therefore has more expressive power than LDA. Currently, there are no documented research results which utilize the PAM technique for topic modeling in song lyrics. Finally, I will obtain a collection of human-annotated songs in order to apply a supervised topic modeling approach and use the results in order to produce one of the topic quality metrics which will be investigated in this paper. 1 Introduction The field of music information retrieval encompasses the research efforts which address several prob- lems, including genre classification, mood categorization, and topic detection in lyrics. The focus of this paper is the latter of these problems. Within the study of topic modeling, topics are defined as a set of k item I = fi1; : : : ; iN g distri- butions P1(I);:::;Pk(I).
    [Show full text]
  • Topic Modeling Based on Keywords and Context
    Topic Modeling based on Keywords and Context Johannes Schneider, University of Liechtenstein Michail Vlachos, IBM Research, Zurich February 6, 2018 Abstract PLSA (and LDA) the frequency of a word within a topic heavily influences its probability (within the Current topic models often suffer from discovering top- topic) whereas the frequencies of the word in other ics not matching human intuition, unnatural switching topics have a lesser impact. Thus, a common word of topics within documents and high computational that occurs equally frequently across all topics might demands. We address these shortcomings by propos- have a large probability in each topic. This makes ing a topic model and an inference algorithm based on sense for models based on document “generation”, be- automatically identifying characteristic keywords for cause frequent words should be generated more often. topics. Keywords influence the topic assignments of But using our rationale that a likely word should also nearby words. Our algorithm learns (key)word-topic be typical for a topic, high scores (or probabilities) scores and self-regulates the number of topics. The should only be assigned to words that occur in a few inference is simple and easily parallelizable. A quali- topics. Although there are techniques that provide tative analysis yields comparable results to those of a weighing of words, they either do not fully cover state-of-the-art models, but with different strengths the above rationale and perform static weighing, e.g., and weaknesses. Quantitative analysis using eight TF-IDF, or they have high computational demands datasets shows gains regarding classification accuracy, but yield limited improvements [14].
    [Show full text]
  • A Latent Semantic Approach Yik-Cheung Tam CMU-LTI-09-016
    Rapid Unsupervised Topic Adaptation – a Latent Semantic Approach Yik-Cheung Tam CMU-LTI-09-016 Language Technologies Institute School of Computer Science Carnegie Mellon University 5000 Forbes Ave., Pittsburgh, PA 15213 www.lti.cs.cmu.edu Thesis Committee: Tanja Schultz (Chair) Alex Waibel Stephan Vogel Sanjeev P. Khudanpur, Johns Hopkins University Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Language and Information Technologies c 2009, Yik-Cheung Tam To Cindy, and my family, iv Abstract In open-domain language exploitation applications, a wide variety of topics with swift topic shifts has to be captured. Consequently, it is crucial to rapidly adapt all language components of a spoken language system. This thesis addresses unsupervised topic adap- tation in both monolingual and crosslingual settings. For automatic speech recognition we rapidly adapt a language model on a source language. For statistical machine translation, we adapt a language model of a target language, a translation lexicon and a phrase table using a source text. For monolingual adaptation, we propose latent Dirichlet-Tree allocation for Bayesian latent semantic analysis. Our model enables rapid incremental language model adaptation via caching the fractional topic counts of word hypotheses decoded from previous speech utterances. Latent Dirichlet-Tree allocation models topic correlation in a tree-based hi- erarchy and thus addresses the model initialization issue. To address the “bag-of-word” assumption in latent semantic analysis, we extend our approach to N-gram latent Dirichlet- Tree allocation. We investigate a fractional Kneser-Ney smoothing approach to handle fractional counts for topic models.
    [Show full text]
  • Efficient Learning for Spoken Language Understanding Tasks with Word Embedding Based Pre-Training
    INTERSPEECH 2015 Efficient learning for spoken language understanding tasks with word embedding based pre-training Yi Luan , Shinji Watanabe, and Bret Harsham Mitsubishi Electric Research Laboratories [email protected], watanabe,harsham @merl.com { } Abstract Intention understanding is framed as a semantic utterance classification problem while goal estimation is framed as clas- Spoken language understanding (SLU) tasks such as goal esti- sification problem of an entire dialog. mation and intention identification from user’s commands are Conventional intention and goal estimation approaches use essential components in spoken dialog systems. In recent years, bag of word (BoW) features (or bag of intention features in goal neural network approaches have shown great success in various estimation) as inputs to standard classification methods such as SLU tasks. However, one major difficulty of SLU is that the boosting, support vector machine, and logistic regression. How- annotation of collected data can be expensive. Often this results ever, one of the problems of applying BoW features to SLU in insufficient data being available for a task. The performance tasks is that the feature vector tends to be very sparse. Each ut- of a neural network trained in low resource conditions is usually terance only includes a dozen words at most (unlike document inferior because of over-training. To improve the performance, analysis). Therefore, the feature vector sometimes lacks enough this paper investigates the use of unsupervised training methods semantic information to accurately estimate user intentions or with large-scale corpora based on word embedding and latent goals. This paper investigates the use of additional semantic in- topic models to pre-train the SLU networks.
    [Show full text]