DOCSLIB.ORG

Duplicate Question Identification by Integrating Framenet with Neural

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Duplicate Question Identification by Integrating Framenet with Neural The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) Duplicate Question Identification by Integrating FrameNet with Neural Networks Xiaodong Zhang,1 Xu Sun,1 Houfeng Wang1,2 1 MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China 2 Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, 221009, China {zxdcs, xusun, wanghf}@pku.edu.cn Abstract • Q3: How can I go downtown from the airport? • Q4: How can I go downtown from the park? There are two major problems in duplicate question identifi- cation, namely lexical gap and essential constituents match- First, different people tend to use different words and ing. Previous methods either design various similarity fea- phrases to express the same meaning. For Q1 and Q2, al- tures or learn representations via neural networks, which though they are semantically equivalent, there are only a few try to solve the lexical gap but neglect the essential con- overlapped words. If only considering the surface form of stituents matching. In this paper, we focus on the essential the questions without leveraging any semantic information, constituents matching problem and use FrameNet-style se- it is hard to identify that they are duplicate. This problem mantic parsing to tackle it. Two approaches are proposed to is called lexical gap. Second, for Q3 and Q4, although most integrate FrameNet parsing with neural networks. An ensem- ble approach combines a traditional model with manually de- words are overlapped and there is only one different word, signed features and a neural network model. An embedding they are not duplicate. There are two essential constituents, approach converts frame parses to embeddings, which are points of departure and destination, in the two questions. The combined with word embeddings at the input of neural net- departure places are different, therefore the answers for one works. Experiments on Quora question pairs dataset demon- question are useless for the other one. It is hard for a model strate that the ensemble approach is more effective and out- to classify them as non-duplicate because their surface forms performs all baselines.1 are so similar. We refer to this problem as essential con- stituents matching. Distributed representation is an effective way to tackle the Introduction lexical gap problem. Researchers have designed various sim- Duplicate question identification (DQI) aims to compare two ilarity features based on word embeddings (Franco-Salvador questions and identify whether they are semantically equiv- et al. 2016), or acquired representations of questions via neu- alent or not, i.e., a binary classification problem. It is a vi- ral networks and then calculated their similarity (Santos et tal task for community question answering (CQA). With an al. 2015; Lei et al. 2016). Although much effort has been automatic DQI method, a CQA forum can merge duplicate paid to the lexical gap problem, there is little research on the questions so as to organize questions and answers more ef- essential constituents matching problem, which is also vital ficiently. Besides, by retrieving questions that are semanti- to DQI. Previous approaches are generally based on simi- cally equivalent to a question presented by a user, an auto- larity. Therefore they are unlikely to classify Q3 and Q4 as matic QA system can answer the user’s question with an- non-duplicate. The words and sentence patterns of the two swers of the retrieved questions. questions are so similar that representations learned by neu- There are two major problems in DQI, namely lexical gap ral networks are likely to be similar. There should be a way (or called semantic gap) and essential constituents matching. to model the matching of essential constituents in question Essential constituents of a question refer to constituents that pairs explicitly. The unmatched essential constituents can are important to the meaning of the question. A constituent provide strong clues for predicting question pairs as non- contains two parts, name and value. For example, for a ques- duplicate. tion asking route, there is usually a destination constituent. It is non-trivial to extract essential constituents in a ques- Four questions are listed below to explain the two prob- tion. A common way is to define constituent categories by lems. The first two questions are duplicate, and the last two experts and label some questions by annotators to get a la- are non-duplicate. beled dataset. Then, a supervised sequence labeling model can be trained on the dataset to extract essential constituents, • Q1: What is the most populous state in the USA? which is similar to named entity recognition task (Sang • Q2: Which state in the United States has the most people? and Meulder 2003). However, defining and labeling essen- Copyright c 2018, Association for the Advancement of Artificial tial constituents in open domain are impractical, consum- Intelligence (www.aaai.org). All rights reserved. ing too much time and funding. Fortunately, there is a cor- 1The code is available at https://github.com/zxdcs/DQI relation between essential constituents and semantic units 6061 in a semantic parse. Hence, we parse questions using a parent. By viewing the name and LUs of a frame as the name FrameNet (Baker, Fillmore, and Lowe 1998) parser and ap- and value of an essential constituent, a frame can be easily proximate essential constituents by frames. The essential converted to an essential constituent. This is the main reason constituents matching problem is transformed into a frame why FrameNet-style parsing is used. matching problem. In this way, manual labeling is avoided We find that the FrameNet parsing cannot cover all es- and all we need is a frame parser, which is publicly available sential constituents in questions, which is because of both on the Internet. the incomplete coverage of FrameNet and unsatisfying per- In this paper, we use FrameNet parsing for essential con- formance of the parser. A major missing is some location stituents matching and neural networks for handling lexical constituents. For example, in question “What is the best gap. Two approaches are proposed to integrate FrameNet travel website in Spain?”, the word Spain is not included parses with neural networks, namely ensemble approach and in any frame. To overcome this shortcoming, named enti- embedding approach. In the ensemble approach, two mod- ties in questions are recognized by an automatic recognizer els are trained separately and their outputs are combined. For and they are fitted into the FrameNet structure. Specifically, FrameNet parses, two kinds of features on the word and the the word Spain is recognized as a geo-political entity (GPE). frame level are designed to measure the matching degrees Hence, a frame with GPE as the name and Spain as the LU of essential constituents. A gradient boosting decision tree is constructed. (GBDT) (Friedman 2001) classifier is trained on these fea- tures. For neural networks, any kinds of neural networks that take two sentences as input can be used. In the embedding Neural Networks Model approach, a unified model is proposed. Each kind of frame is assigned to an embedding. The frame embeddings are con- Neural networks models (NNMs) with word embeddings as catenated with word embeddings at the input of neural net- input are ideal models to handle the lexical gap problem. works. Consequently, the representations learned by neural Because our focus is how to leverage frame parsing rather networks can include essential constituents information. than proposing a novel NNM, we directly use some off-the- shelf models that perform well on similar tasks. In exper- Frame Parsing iments, many different kinds of neural networks are tried. The FrameNet project (Baker, Fillmore, and Lowe 1998) is a Here we only introduce a basic one as an example, that is semantic database of English, which contains about 200,000 a Siamese network (Bromley et al. 1993) consisting of two manually annotated sentences linked to more than 1,200 se- bidirectional long short-term memory (BLSTM) networks. mantic frames. It is based on a theory of meaning called The structure is shown in Figure 1. Frame Semantics (Fillmore 1976). The basic idea is that the meaning of most words can be understood on the basis of se- y mantic frames, which are represented by three major compo- nents: frame, frame elements (FEs) and lexical units (LUs). Table 1 lists the parse of Q3 using a FrameNet parser MLP called SEMAFOR (Kshirsagar et al. 2015). The first column lists all words in the question. In the rest columns, each col- J umn represents a frame. The word that triggers a frame is marked by bold text, and other items represent FEs of the frame. The question contains three frames, including capa- H1 H2 bility, motion and buildings. Take the motion frame as an example, it is evoked by LU go and contains three FEs, i.e., theme, goal and source. The FE goal is filled by LU down- town. Capability Motion Buildings E1 E2 How Entity Q1 Q2 can Capability … the most populous… … has the most people… I Event Theme go Motion Figure 1: The structure of the BLSTM model. downtown Goal from At first, a question Q =[w1,w2, ..., wn] is mapped the Source to an embedding matrix E via lookup table, i.e. E = airport Buildings [e(w1),e(w2), ..., e(wn)], where e(wt) is the word embed- ? ding of wt. Then a BLSTM (Hochreiter and Schmidhuber 1997; Graves 2012) is employed to learn contextual repre- Table 1: FrameNet-style parsing of a question. sentations of the embeddings and these representations are reduced to a fixed-length representation H. The gates, cell The resemblance of frame and essential constituent is ap- and output of LSTM are calculated as follows: 6062 For example, if fi is Capability in Table 1, ri is {How, can, I}.
Load more

Recommended publications
  • WELDA: Enhancing Topic Models by Incorporating Local Word Context
    WELDA: Enhancing Topic Models by Incorporating Local Word Context Stefan Bunk Ralf Krestel Hasso Plattner Institute Hasso Plattner Institute Potsdam, Germany Potsdam, Germany [email protected] [email protected] ABSTRACT The distributional hypothesis states that similar words tend to have similar contexts in which they occur. Word embedding models exploit this hypothesis by learning word vectors based on the local context of words. Probabilistic topic models on the other hand utilize word co-occurrences across documents to identify topically related words. Due to their complementary nature, these models define different notions of word similarity, which, when combined, can produce better topical representations. In this paper we propose WELDA, a new type of topic model, which combines word embeddings (WE) with latent Dirichlet allo- cation (LDA) to improve topic quality. We achieve this by estimating topic distributions in the word embedding space and exchanging selected topic words via Gibbs sampling from this space. We present an extensive evaluation showing that WELDA cuts runtime by at least 30% while outperforming other combined approaches with Figure 1: Illustration of an LDA topic in the word embed- respect to topic coherence and for solving word intrusion tasks. ding space. The gray isolines show the learned probability distribution for the topic. Exchanging topic words having CCS CONCEPTS low probability in the embedding space (red minuses) with • Information systems → Document topic models; Document high probability words (green pluses), leads to a superior collection models; • Applied computing → Document analysis; LDA topic. KEYWORDS word by the company it keeps” [11]. In other words, if two words topic models, word embeddings, document representations occur in similar contexts, they are similar.
    [Show full text]
  • Generative Topic Embedding: a Continuous Representation of Documents
    Generative Topic Embedding: a Continuous Representation of Documents Shaohua Li1,2 Tat-Seng Chua1 Jun Zhu3 Chunyan Miao2 [email protected] [email protected] [email protected] [email protected] 1. School of Computing, National University of Singapore 2. Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) 3. Department of Computer Science and Technology, Tsinghua University Abstract as continuous vectors in a low-dimensional em- bedding space (Bengio et al., 2003; Mikolov et al., Word embedding maps words into a low- 2013; Pennington et al., 2014; Levy et al., 2015). dimensional continuous embedding space The learned embedding for a word encodes its by exploiting the local word collocation semantic/syntactic relatedness with other words, patterns in a small context window. On by utilizing local word collocation patterns. In the other hand, topic modeling maps docu- each method, one core component is the embed- ments onto a low-dimensional topic space, ding link function, which predicts a word’s distri- by utilizing the global word collocation bution given its context words, parameterized by patterns in the same document. These their embeddings. two types of patterns are complementary. When it comes to documents, we wish to find a In this paper, we propose a generative method to encode their overall semantics. Given topic embedding model to combine the the embeddings of each word in a document, we two types of patterns. In our model, topics can imagine the document as a “bag-of-vectors”. are represented by embedding vectors, and Related words in the document point in similar di- are shared across documents.
    [Show full text]
  • Linked Data Triples Enhance Document Relevance Classification
    applied sciences Article Linked Data Triples Enhance Document Relevance Classification Dinesh Nagumothu * , Peter W. Eklund , Bahadorreza Ofoghi and Mohamed Reda Bouadjenek School of Information Technology, Deakin University, Geelong, VIC 3220, Australia; [email protected] (P.W.E.); [email protected] (B.O.); [email protected] (M.R.B.) * Correspondence: [email protected] Abstract: Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news- feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments Citation: Nagumothu, D.; Eklund, indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% P.W.; Ofoghi, B.; Bouadjenek, M.R.
    [Show full text]
  • Document and Topic Models: Plsa
    10/4/2018 Document and Topic Models: pLSA and LDA Andrew Levandoski and Jonathan Lobo CS 3750 Advanced Topics in Machine Learning 2 October 2018 Outline • Topic Models • pLSA • LSA • Model • Fitting via EM • pHITS: link analysis • LDA • Dirichlet distribution • Generative process • Model • Geometric Interpretation • Inference 2 1 10/4/2018 Topic Models: Visual Representation Topic proportions and Topics Documents assignments 3 Topic Models: Importance • For a given corpus, we learn two things: 1. Topic: from full vocabulary set, we learn important subsets 2. Topic proportion: we learn what each document is about • This can be viewed as a form of dimensionality reduction • From large vocabulary set, extract basis vectors (topics) • Represent document in topic space (topic proportions) 푁 퐾 • Dimensionality is reduced from 푤푖 ∈ ℤ푉 to 휃 ∈ ℝ • Topic proportion is useful for several applications including document classification, discovery of semantic structures, sentiment analysis, object localization in images, etc. 4 2 10/4/2018 Topic Models: Terminology • Document Model • Word: element in a vocabulary set • Document: collection of words • Corpus: collection of documents • Topic Model • Topic: collection of words (subset of vocabulary) • Document is represented by (latent) mixture of topics • 푝 푤 푑 = 푝 푤 푧 푝(푧|푑) (푧 : topic) • Note: document is a collection of words (not a sequence) • ‘Bag of words’ assumption • In probability, we call this the exchangeability assumption • 푝 푤1, … , 푤푁 = 푝(푤휎 1 , … , 푤휎 푁 ) (휎: permutation) 5 Topic Models: Terminology (cont’d) • Represent each document as a vector space • A word is an item from a vocabulary indexed by {1, … , 푉}. We represent words using unit‐basis vectors.
    [Show full text]
  • Improving Distributed Word Representation and Topic Model by Word-Topic Mixture Model
    JMLR: Workshop and Conference Proceedings 63:190–205, 2016 ACML 2016 Improving Distributed Word Representation and Topic Model by Word-Topic Mixture Model Xianghua Fu [email protected] Ting Wang [email protected] Jing Li [email protected] Chong Yu [email protected] Wangwang Liu [email protected] College of Computer Science and Software Engineering, Shenzhen University, Guangdong China Editors: Robert J. Durrant and Kee-Eung Kim Abstract We propose a Word-Topic Mixture(WTM) model to improve word representation and topic model simultaneously. Firstly, it introduces the initial external word embeddings into the Topical Word Embeddings(TWE) model based on Latent Dirichlet Allocation(LDA) model to learn word embeddings and topic vectors. Then the results learned from TWE are inte- grated in the LDA by defining the probability distribution of topic vectors-word embeddings according to the idea of latent feature model with LDA (LFLDA), meanwhile minimizing the KL divergence of the new topic-word distribution function and the original one. The experimental results prove that the WTM model performs better on word representation and topic detection compared with some state-of-the-art models. Keywords: topic model, distributed word representation, word embedding, Word-Topic Mixture model 1. Introduction Probabilistic topic model is one of the most common topic detection methods. Topic mod- eling algorithms, such as LDA(Latent Dirichlet Allocation) Blei et al. (2003) and related methods Blei (2011), infer probability distributions from frequency statistic, which can only reflect the co-occurrence relationships of words. The semantic information is less considered. Recently, distributed word representations with NNLM(neural network language model) Bengio et al.
    [Show full text]
  • Topic Modeling: Beyond Bag-Of-Words
    Topic Modeling: Beyond Bag-of-Words Hanna M. Wallach [email protected] Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK Abstract While such models may use conditioning contexts of arbitrary length, this paper deals only with bigram Some models of textual corpora employ text models—i.e., models that predict each word based on generation methods involving n-gram statis- the immediately preceding word. tics, while others use latent topic variables inferred using the “bag-of-words” assump- To develop a bigram language model, marginal and tion, in which word order is ignored. Pre- conditional word counts are determined from a corpus viously, these methods have not been com- w. The marginal count Ni is defined as the number bined. In this work, I explore a hierarchi- of times that word i has occurred in the corpus, while cal generative probabilistic model that in- the conditional count Ni|j is the number of times word corporates both n-gram statistics and latent i immediately follows word j. Given these counts, the topic variables by extending a unigram topic aim of bigram language modeling is to develop pre- model to include properties of a hierarchi- dictions of word wt given word wt−1, in any docu- cal Dirichlet bigram language model. The ment. Typically this is done by computing estimators model hyperparameters are inferred using a of both the marginal probability of word i and the con- Gibbs EM algorithm. On two data sets, each ditional probability of word i following word j, such as of 150 documents, the new model exhibits fi = Ni/N and fi|j = Ni|j/Nj, where N is the num- better predictive accuracy than either a hi- ber of words in the corpus.
    [Show full text]
  • Semantic N-Gram Topic Modeling
    EAI Endorsed Transactions on Scalable Information Systems Research Article Semantic N-Gram Topic Modeling Pooja Kherwa1,*, Poonam Bansal1 1Maharaja Surajmal Institute of Technology, C-4 Janak Puri. GGSIPU. New Delhi-110058, India. Abstract In this paper a novel approach for effective topic modeling is presented. The approach is different from traditional vector space model-based topic modeling, where the Bag of Words (BOW) approach is followed. The novelty of our approach is that in phrase-based vector space, where critical measure like point wise mutual information (PMI) and log frequency based mutual dependency (LGMD)is applied and phrase’s suitability for particular topic are calculated and best considerable semantic N-Gram phrases and terms are considered for further topic modeling. In this experiment the proposed semantic N-Gram topic modeling is compared with collocation Latent Dirichlet allocation(coll-LDA) and most appropriate state of the art topic modeling technique latent Dirichlet allocation (LDA). Results are evaluated and it was found that perplexity is drastically improved and found significant improvement in coherence score specifically for short text data set like movie reviews and political blogs. Keywords. Topic Modeling, Latent Dirichlet Allocation, Point wise Mutual Information, Bag of words, Coherence, Perplexity. Received on 16 November 2019, accepted on 10 0Februar20 y 2 , published on 11 February 2020 Copyright © 2020 Pooja Kherwa et al., licensed to EAI. T his is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
    [Show full text]
  • Modeling Topical Relevance for Multi-Turn Dialogue Generation
    Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) Modeling Topical Relevance for Multi-Turn Dialogue Generation Hainan Zhang2∗ , Yanyan Lan14y , Liang Pang14 , Hongshen Chen2 , Zhuoye Ding2 and Dawei Yin4 1CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, CAS 2JD.com, Beijing, China 3Baidu.com, Beijing, China 4University of Chinese Academy of Sciences fzhanghainan6,chenhongshen,[email protected], flanyanyan,[email protected],[email protected] Abstract context1 `}，(吗？(Hello) context2 有什么问题我可以.¨b? (What can I do for you?) FÁM价了，我要3请保价 Topic drift is a common phenomenon in multi-turn context3 (The product price has dropped. I want a low-price.) dialogue. Therefore, an ideal dialogue generation }的，这边.¨3请，FÁ已Ï60了'？ context4 models should be able to capture the topic informa- (Ok, I will apply for you. Have you received the product?) 东西60了发h不(一w吗？ tion of each context, detect the relevant context, and context5 (I have received the product without the invoice together.) produce appropriate responses accordingly. How- 开w5P发h不会随'Äú context6 ever, existing models usually use word or sentence (The electronic invoice will not be shipped with the goods.) level similarities to detect the relevant contexts, current context /发我®±吗？(Is it sent to my email？) /的，请¨Ð供®±0@，5P发h24小时Äú。 which fail to well capture the topical level rele- response (Yes, please provide your email address， vance. In this paper, we propose a new model, we will send the electronic invoices in 24 hours.) named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole Table 1: The example from the customer services dataset.
    [Show full text]
  • Text Analysis of QC/Issue Tracker by Natural Language Processing (NLP)
    PharmaSUG 2019 - Paper AD-052 Text Analysis of QC/Issue Tracker by Natural Language Processing (NLP) Tools Huijuan Zhang, Columbia University, New York, NY Todd Case, Vertex Pharmaceuticals, Boston, MA ABSTRACT Many Statistical Programming groups use QC and issue trackers, which typically include text that describe discrepancies or other notes documented during programming and QC, or 'data about the data'. Each text field is associated with one specific question or problem and/or manually classified into a category and a subcategory by programmers (either by free text or a drop-down box with pre-specified issues that are common, such as 'Specs', 'aCRF', 'SDTM', etc.). Our goal is to look at this text data using some Natural Language Processing (NLP) tools. Using NLP tools allows us an opportunity to be more objective about finding high-level (and even granular) themes about our data by using algorithms that parse out categories, typically either by pre-specified number of categories or by summarizing the most common words. Most importantly, using NLP we do not have to look at text line by line, but it rather provides us an idea about what this entire text is telling us about our processes and issues at a high-level, even checking whether the problems were classified correctly and classifying problems that were not classified previously (e.g., maybe a category was forgotten, it didn't fit into a pre-specified category, etc.). Such techniques provide unique insights into data about our data and the possibility to replace manual work, thus improving work efficiency. INTRODUCTION The purpose of this paper is to demonstrate how NLP tools can be used in a very practical way: to find trends in data that we would not otherwise be able to find using more traditional approaches.
    [Show full text]
  • Topic Modeling Approaches for Understanding COVID-19
    Topic Modeling Approaches for Understanding COVID-19 Misinformation Spread in Sub-Saharan Africa Ezinne Nwankwo1∗ , Chinasa Okolo2 and Cynthia Habonimana3 1Duke University 2Cornell University 3CSU-Long Beach [email protected], [email protected], [email protected] Abstract [1]. Fake news is often spread with the intent to provide financial or political gain and is often exacerbated by digi- Since the start of the pandemic, the proliferation tal illiteracy, lack of knowledge surrounding specific topics, of fake news and misinformation has been a con- and religious or personal beliefs that are biased towards fake stant battle for health officials and policy makers as news [2]. To get a sense of what are common themes in the they work to curb the spread of COVID-19. In ar- “fake news” being spread across the African continent and in eas within the Global South, it can be difficult for other developing nations, we thought it would be best to dis- officials to keep track of the growth of such false in- cover what ideologies they are rooted in. From our research, formation and even harder to address the real con- we saw three main themes in fake news being spread across cerns their communities have. In this paper, we Sub-Saharan Africa: distrust of philanthropic organizations, present some techniques the AI community can of- distrust of developed nations, and even distrust of leaders in fer to help address this issue. While the topics pre- their own respective countries. We posit that the distrust in sented within this paper are not a complete solution, these messages stems from the ”colonial-era-driven distrust” we believe they could complement the work gov- of international development organizations [3][4].
    [Show full text]
  • Distributional Approaches to Semantic Analysis
    Distributional approaches to semantic analysis Diarmuid O´ S´eaghdha Natural Language and Information Processing Group Computer Laboratory University of Cambridge [email protected] HIT-MSRA Summer Workshop on Human Language Technology August 2011 http://www.cl.cam.ac.uk/~do242/Teaching/HIT-MSRA-2011/ Your assignment (Part 2) I We now have two sets of word pairs with similarity judgements: one in English and one in Chinese. They are available on the summer school website, along with a more detailed specification of the assignment. I The task is to take some real corpora and build distributional models, exploring the space of design options that have been sketched in this course: I What is the effect of different context types (e.g., window context versus document context)? I What is the effect of different similarity and distance functions (e.g., cosine and Jensen-Shannon divergence)? I What is the effect of corpus size? I What is the effect of applying association measures such as PMI? I If you do this assignment in full, you will have a good idea how to build a distributional model to help with the NLP tasks you care about! Distributional approaches to semantic analysis Fundamentals of distributional methods 58 Probabilistic semantics and topic modelling What's in today's lecture: A probabilistic interpretation of distributional semantics Topic modelling Selectional preferences Word sense disambiguation Distributional approaches to semantic analysis Probabilistic semantics and topic modelling 59 Probabilistic modelling I Probabilistic modelling (or statistical modelling) explicitly recasts the problem of interest as one of inferring probability distributions over random variables and estimating properties of those distributions.
    [Show full text]
  • Topic Modeling for Natural Language Understanding
    Topic Modeling for Natural Language Understanding A Thesis Submitted to the Faculty of Drexel University by Xiaoli Song in partial fulfillment of the requirements for the degree of Doctor of Philosophy December 2016 c Copyright 2016 Xiaoli Song. ii Acknowledgments Thank God for giving me all the help, strength and determination to complete my thesis. I thank my advisor Dr. Xiaohua Hu. His guidance helped to shape and provided much needed focus to my work. I thank my dissertation committee members: Dr. Weimao Ke, Dr. Yuan An, Dr. Erjia Yan and Dr. Li Sheng for their support and insight throughout my research. I would also like to thank my fellow graduate students Zunyan Xiong, Jia Huang, Wanying Ding, Yue Shang, Mengwen Liu, Yuan Ling, Bo Song, and Yizhou Zang for all the help and support they provided. Special thanks to Chunyu Zhao, Yujia Wang, and Weilun Cheng. iii Table of Contents LIST OF TABLES ............................................. vii LIST OF FIGURES ............................................. ix ABSTRACT ................................................ x 1. INTRODUCTION ............................................ 1 1.1 Overview ............................................. 2 2. CLASSICAL TOPIC MODELING METHODS ............................. 4 2.0.1 Latent Semantic Analysis (LSA)............................... 4 2.0.2 PLSA ............................................. 5 2.1 Latent Dirichlet Allocation.................................... 6 3. PAIRWISE TOPIC MODEL ...................................... 8 3.1 Pairwise Relation
    [Show full text]