DOCSLIB.ORG

SPRITE: Generalizing Topic Models with Structured Priors

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
SPRITE: Generalizing Topic Models with Structured Priors SPRITE: Generalizing Topic Models with Structured Priors Michael J. Paul and Mark Dredze Department of Computer Science Human Language Technology Center of Excellence Johns Hopkins University, Baltimore, MD 21218 [email protected], [email protected] Abstract between topics and document attributes (Ramage et al., 2009; Mimno and McCallum, 2008). We introduce SPRITE, a family of topic SPRITE builds on a standard topic model, models that incorporates structure into adding structure to the priors over the model pa- model priors as a function of underlying rameters. The priors are given by log-linear func- components. The structured priors can tions of underlying components ( 2), which pro- § be constrained to model topic hierarchies, vide additional latent structure that we will show factorizations, correlations, and supervi- can enrich the model in many ways. By apply- sion, allowing SPRITE to be tailored to ing particular constraints and priors to the compo- particular settings. We demonstrate this nent hyperparameters, a variety of structures can flexibility by constructing a SPRITE-based be induced such as hierarchies and factorizations model to jointly infer topic hierarchies and ( 3), and we will show that this framework cap- § author perspective, which we apply to cor- tures many existing topic models ( 4). § pora of political debates and online re- After describing the general form of the model, views. We show that the model learns in- we show how SPRITE can be tailored to partic- tuitive topics, outperforming several other ular settings by describing a specific model for topic models at predictive tasks. the applied task of jointly inferring topic hierar- chies and perspective ( 6). We experiment with § 1 Introduction this topic+perspective model on sets of political debates and online reviews ( 7), and demonstrate Topic models can be a powerful aid for analyzing § large collections of text by uncovering latent in- that SPRITE learns desired structures while outper- terpretable structures without manual supervision. forming many baselines at predictive tasks. Yet people often have expectations about topics in 2 Topic Modeling with Structured Priors a given corpus and how they should be structured for a particular task. It is crucial for the user expe- Our model family generalizes latent Dirichlet al- rience that topics meet these expectations (Mimno location (LDA) (Blei et al., 2003b). Under LDA, et al., 2011; Talley et al., 2011) yet black box topic there are K topics, where a topic is a categor- models provide no control over the desired output. ical distribution over V words parameterized by This paper presents SPRITE, a family of topic φk. Each document has a categorical distribution models that provide a flexible framework for en- over topics, parameterized by θm for the mth doc- coding preferences as priors for how topics should ument. Each observed word in a document is gen- be structured. SPRITE can incorporate many types erated by drawing a topic z from θm, then drawing of structure that have been considered in prior the word from φz. θ and φ have priors given by work, including hierarchies (Blei et al., 2003a; Dirichlet distributions. Mimno et al., 2007), factorizations (Paul and Our generalization adds structure to the gener- Dredze, 2012; Eisenstein et al., 2011), sparsity ation of the Dirichlet parameters. The priors for (Wang and Blei, 2009; Balasubramanyan and Co- these parameters are modeled as log-linear com- hen, 2013), correlations between topics (Blei and binations of underlying components. Components Lafferty, 2007; Li and McCallum, 2006), pref- are real-valued vectors of length equal to the vo- erences over word choices (Andrzejewski et al., cabulary size V (for priors over word distribu- 2009; Paul and Dredze, 2013), and associations tions) or length equal to the number of topics K 43 Transactions of the Association for Computational Linguistics, vol. 3, pp. 43–57, 2015. Action Editor: Janyce Wiebe. Submission batch: 7/2014; Revision batch 12/2014; Published 1/2015. c 2015 Association for Computational Linguistics. Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00121 by guest on 24 September 2021 Generate hyperparameters: α, β, δ, ω ( 3) (for priors over topic distributions). • § For each document m, generate parameters: For example, we might assume that topics about • sports like baseball and football share a common ˜ C(θ) 1. θmk = exp( c=1 αmc δck), 1 k K prior – given by a component – with general words ≤ ≤ 2. θ Dirichlet(θ˜ ) about sports. A fine-grained topic about steroid m ∼ P m For each topic k, generate parameters: use in sports might be created by combining com- • ponents about broader topics like sports, medicine, C(φ) 1. φ˜kv = exp( βkc ωcv), 1 v V and crime. By modeling the priors as combina- c=1 ≤ ≤ 2. φ Dirichlet(φ˜ ) tions of components that are shared across all top- k ∼ P k ics, we can learn interesting connections between For each token (m, n), generate data: • topics, where components provide an additional 1. Topic (unobserved): zm,n θm latent layer for corpus understanding. ∼ 2. Word (observed): wm,n φzm,n As we’ll show in the next section, by imposing ∼ certain requirements on which components feed Figure 1: The generative story of SPRITE. The difference into which topics (or documents), we can induce from latent Dirichlet allocation (Blei et al., 2003b) is the gen- eration of the Dirichlet parameters. a variety of model structures. For example, if we want to model a topic hierarchy, we require that each topic depend on exactly one parent compo- guage” topic component, with high-weight words nent. If we want to jointly model topic and ide- such as speech and spoken, which informs the ology in a corpus of political documents ( 6), we prior of all three topics. Second, we can model that § make topic priors a combination of one component these topics are likely to occur together in docu- from each of two groups: a topical component and ments. For example, articles about dialog systems an ideological component, resulting in ideology- are likely to discuss automatic speech recognition specific topics like “conservative economics”. as a subroutine. This is handled by the document Components construct priors as follows. For the components – there could be a “spoken language” topic-specific word distributions φ, there are C(φ) document component that gives high weight to all topic components. The kth topic’s prior over φk three topics, so that if a document draw its prior is a weighted combination (with coefficient vector from this component, then it is more likely to give (φ) βk) of the C components (where component c is probability to these topics together. denoted ωc). For the document-specific topic dis- The next section will describe how particular tributions θ, there are C(θ) document components. priors over the coefficients can induce various The mth document’s prior over θm is a weighted structures such as hierarchies and factorizations, (θ) combination (coefficients αm) of the C compo- and components and coefficients can also be pro- nents (where component c is denoted δc). vided as input to incorporate supervision and prior Once conditioned on these priors, the model knowledge. The general prior structure used in is identical to LDA. The generative story is de- SPRITE can be used to represent a wide array of scribed in Figure 1. We call this family of models existing topic models, outlined in Section 4. SPRITE: Structured PRIor Topic modEls. 3 Topic Structures To illustrate the role that components can play, consider an example in which we are modeling re- By changing the particular configuration of the hy- search topics in a corpus of NLP abstracts (as we perparameters – the component coefficients (α and do in 7.3). Consider three speech-related topics: β) and the component weights (δ and ω) – we ob- § signal processing, automatic speech recognition, tain a diverse range of model structures and behav- and dialog systems. Conceptualized as a hierar- iors. We now describe possible structures and the chy, these topics might belong to a higher level corresponding priors. category of spoken language processing. SPRITE allows the relationship between these three topics 3.1 Component Structures to be defined in two ways. One, we can model that This subsection discusses various graph structures these topics will all have words in common. This that can describe the relation between topic com- is handled by the topic components – these three ponents and topics, and between document com- topics could all draw from a common “spoken lan- ponents and documents, illustrated in Figure 2. 44 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00121 by guest on 24 September 2021 (a) Dense DAG (b) Sparse DAG (c) Tree (d) Factored Forest Figure 2: Example graph structures describing possible relations between components (middle row) and topics or documents (bottom row). Edges correspond to non-zero values for α or β (the component coefficients defining priors over the document and topic distributions). The root node is a shared prior over the component weights (with other possibilities discussed in 3.3). § 3.1.1 Directed Acyclic Graph For a weighted tree, α and β could be a product The general SPRITE model can be thought of as a of two variables: an “integer-like” indicator vec- dense directed acyclic graph (DAG), where every tor with sparse Dirichlet prior as suggested above, document or topic is connected to every compo- combined with a real-valued weight (e.g., with a nent with some weight α or β. When many of Gaussian prior). We take this approach in our model of topic and perspective ( 6). the α or β coefficients are zero, the DAG becomes § sparse. A sparse DAG has an intuitive interpre- 3.1.3 Factored Forest tation: each document or topic depends on some subset of components.
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]
  • 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.
    [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]