DOCSLIB.ORG

A Neural Model for Coherent Topic Segmentation and Classification

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Neural Model for Coherent Topic Segmentation and Classification SECTOR: A Neural Model for Coherent Topic Segmentation and Classification Sebastian Arnold Philippe Cudre-Mauroux´ Felix A. Gers Rudolf Schneider University of Fribourg Alexander Loser¨ Beuth University of Applied Fribourg, Switzerland Beuth University of Applied Sciences Berlin, Germany [email protected] Sciences Berlin, Germany fsarnold, ruschneiderg@ fgers, aloeserg@ beuth-hochschule.de beuth-hochschule.de Abstract words or full sentences (Hirschberg and Manning, 2015). Recent neural architectures build upon pre- When searching for information, a human trained word or sentence embeddings (Mikolov reader first glances over a document, spots et al., 2013; Le and Mikolov, 2014), which focus relevant sections, and then focuses on a few on semantic relations that can be learned from sentences for resolving her intention. How- large sets of paradigmatic examples, even from ever, the high variance of document structure long ranges (Dieng et al., 2017). complicates the identification of the salient From a human perspective, however, it is topic of a given section at a glance. To mostly the authors themselves who help best to tackle this challenge, we present SECTOR, a understand a text. Especially in long documents, model to support machine reading systems by an author thoughtfully designs a readable structure segmenting documents into coherent sections and guides the reader through the text by arranging and assigning topic labels to each section. topics into coherent passages (Glavasˇ et al., 2016). Our deep neural network architecture learns a In many cases, this structure is not formally ex- latent topic embedding over the course of a pressed as section headings (e.g., in news articles, document. This can be leveraged to classify reviews, discussion forums) or it is structured local topics from plain text and segment a according to domain-specific aspects (e.g., health document at topic shifts. In addition, we reports, research papers, insurance documents). contribute WikiSection, a publicly available Ideally, systems for text analytics, such as topic data set with 242k labeled sections in English detection and tracking (TDT) (Allan, 2002), text and German from two distinct domains: dis- summarization (Huang et al., 2003), information eases and cities. From our extensive evaluation retrieval (IR) (Dias et al., 2007), or question an- of 20 architectures, we report a highest score swering (QA) (Cohen et al., 2018), could access of 71.6% F1 for the segmentation and a document representation that is aware of both classification of 30 topics from the English topical (i.e., latent semantic content) and struc- city domain, scored by our SECTOR long tural information (i.e., segmentation) in the text short-term memory model with Bloom filter (MacAvaney et al., 2018). The challenge in embeddings and bidirectional segmentation. building such a representation is to combine these This is a significant improvement of 29.5 two dimensions that are strongly interwoven in points F1 over state-of-the-art CNN classifiers the author’s mind. It is therefore important to with baseline segmentation. understand topic segmentation and classification as a mutual task that requires encoding both topic 1 Introduction information and document structure coherently. 1 Today’s systems for natural language understand- In this paper, we present SECTOR, an end-to-end ing are composed of building blocks that extract model that learns an embedding of latent topics semantic information from the text, such as named entities, relations, topics, or discourse structure. 1Our source code is available under the Apache License In traditional natural language processing (NLP), 2.0 at https://github.com/sebastianarnold/ these extractors are typically applied to bags of SECTOR. 169 Transactions of the Association for Computational Linguistics, vol. 7, pp. 169–184, 2019. Action Editor: Radu Florian. Submission batch: 11/2018; Revision batch: 1/2019; Published 4/2019. c 2019 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license. from potentially ambiguous headings and can be mentary: Diseases is a typical scientific domain applied to entire documents to predict local topics with low entropy (i.e., very narrow topics, precise on sentence level. Our model encodes topical in- language, and low word ambiguity). In contrast, formation on a vertical dimension and structural cities resembles a diversified domain, with high information on a horizontal dimension. We show entropy (i.e., broader topics, common language, that the resulting embedding can be leveraged in and higher word ambiguity) and will be more a downstream pipeline to segment a document applicable to for example, news, risk reports, or into coherent sections and classify the sections travel reviews. into one of up to 30 topic categories reaching We compare SECTOR to existing segmenta- 71.6% F1—or alternatively, attach up to 2.8k topic tion and classification methods based on latent labels with 71.1% mean average precision (MAP). Dirichlet allocation (LDA), paragraph embed- We further show that segmentation performance dings, convolutional neural networks (CNNs), and of our bidirectional long short-term memory recurrent neural networks (RNNs). We show that (LSTM) architecture is comparable to specialized SECTOR significantly improves these methods in state-of-the-art segmentation methods on various a combined task by up to 29.5 points F1 when real-world data sets. applied to plain text with no given segmentation. To the best of our knowledge, the combined task The rest of this paper is structured as follows: of segmentation and classification has not been We introduce related work in Section 2. Next, we approached on the full document level before. describe the task and data set creation process in There exist a large number of data sets for text Section 3. We formalize our model in Section 4. segmentation, but most of them do not reflect We report results and insights from the evaluation real-world topic drifts (Choi, 2000; Sehikh et al., in Section 5. Finally, we conclude in Section 6. 2017), do not include topic labels (Eisenstein and Barzilay, 2008; Jeong and Titov, 2010; Glavasˇ 2 Related Work et al., 2016), or are heavily normalized and too small to be used for training neural networks (Chen The analysis of emerging topics over the course et al., 2009). We can utilize a generic segmentation of a document is related to a large number of data set derived from Wikipedia that includes research areas. In particular, topic modeling (Blei headings (Koshorek et al., 2018), but there is also et al., 2003) and TDT (Jin et al., 1999) focus on a need in IR and QA for supervised structural representing and extracting the semantic topical con- topic labels (Agarwal and Yu, 2009; MacAvaney tent of text. Text segmentation (Beeferman et al. et al., 2018), different languages and more specific 1999) is used to split documents into smaller co- domains, such as clinical or biomedical research herent chunks. Finally, text classification (Joachims (Tepper et al., 2012; Tsatsaronis et al., 2012), 1998) is often applied to detect topics on text and news-based TDT (Kumaran and Allan, 2004; chunks. Our method unifies those strongly inter- Leetaru and Schrodt, 2013). woven tasks and is the first to evaluate the com- 2 Therefore we introduce WIKISECTION, a large bined topic segmentation and classification task novel data set of 38k articles from the English using a corresponding data set with long structured and German Wikipedia labeled with 242k sec- documents. tions, original headings, and normalized topic Topic modeling is commonly applied to entire labels for up to 30 topics from two domains: dis- documents using probabilistic models, such as eases and cities. We chose these subsets to cover LDA (Blei et al., 2003). AlSumait et al. (2008) both clinical/biomedical aspects (e.g., symptoms, introduced an online topic model that captures treatments, complications) and news-based topics emerging topics when new documents appear. (e.g., history, politics, economy, climate). Both Gabrilovich and Markovitch (2007) proposed article types are reasonably well-structured ac- the Explicit Semantic Analysis method in which cording to Wikipedia guidelines (Piccardi et al., concepts from Wikipedia articles are indexed and 2018), but we show that they are also comple- assigned to documents. Later, and to overcome the vocabulary mismatch problem, Cimiano et al. (2009) introduced a method for assigning latent 2The data set is available under the CC BY-SA 3.0 license at https://github.com/sebastianarnold/ concepts to documents. More recently, Liu et al. WikiSection. (2016) represented documents with vectors of 170 closely related domain keyphrases. Yeh et al. that includes section headings. The authors intro- (2016) proposed a conceptual dynamic LDA duced a neural architecture for segmentation that model for tracking topics in conversations. Bhatia is based on sentence embeddings and four layers et al. (2016) utilized Wikipedia document titles of bidirectional LSTM. Similar to Sehikh et al. to learn neural topic embeddings and assign doc- (2017), the authors used a binary segmentation ument labels. Dieng et al. (2017) focused on the objective on the sentence level, but trained on issue of long-range dependencies and proposed a entire documents. Our work takes up this idea of latent topic model based on RNNs. However, the end-to-end training and enriches the neural model authors did not apply the RNN to predict local with a layer of latent topic embeddings that can topics. be utilized for topic classification. Text segmentation has been approached Text classification is mostly applied at the with a wide variety of methods. Early unsuper- paragraph or sentence level using machine vised methods utilized lexical overlap statistics learning methods such as support vector machines (Hearst 1997; Choi 2000), dynamic programming (Joachims, 1998) or, more recently, shallow and (Utiyama and Isahara, 2001), Bayesian models deep neural networks (Le et al., 2018; Conneau (Eisenstein and Barzilay, 2008), or pointwise et al., 2017). Notably, paragraph vectors (Le and boundary sampling (Du et al., 2013) on raw terms.
Load more

Recommended publications
  • Sentence Boundary Detection for Handwritten Text Recognition Matthias Zimmermann
    Sentence Boundary Detection for Handwritten Text Recognition Matthias Zimmermann To cite this version: Matthias Zimmermann. Sentence Boundary Detection for Handwritten Text Recognition. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). inria-00103835 HAL Id: inria-00103835 https://hal.inria.fr/inria-00103835 Submitted on 5 Oct 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Sentence Boundary Detection for Handwritten Text Recognition Matthias Zimmermann International Computer Science Institute Berkeley, CA 94704, USA [email protected] Abstract 1) The summonses say they are ” likely to persevere in such In the larger context of handwritten text recognition sys- unlawful conduct . ” <s> They ... tems many natural language processing techniques can 2) ” It comes at a bad time , ” said Ormston . <s> ”A singularly bad time ... potentially be applied to the output of such systems. How- ever, these techniques often assume that the input is seg- mented into meaningful units, such as sentences. This pa- Figure 1. Typical ambiguity for the position of a sen- per investigates the use of hidden-event language mod- tence boundary token <s> in the context of a period els and a maximum entropy based method for sentence followed by quotes.
    [Show full text]
  • Multiple Segmentations of Thai Sentences for Neural Machine Translation
    Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), pages 240–244 Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC Multiple Segmentations of Thai Sentences for Neural Machine Translation Alberto Poncelas1, Wichaya Pidchamook2, Chao-Hong Liu3, James Hadley4, Andy Way1 1ADAPT Centre, School of Computing, Dublin City University, Ireland 2SALIS, Dublin City University, Ireland 3Iconic Translation Machines 4Trinity Centre for Literary and Cultural Translation, Trinity College Dublin, Ireland {alberto.poncelas, andy.way}@adaptcentre.ie [email protected], [email protected], [email protected] Abstract Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool. Keywords: Machine Translation, Word Segmentation, Thai Language In Machine Translation (MT), low-resource languages are 1. Combination of Segmented Texts especially challenging as the amount of parallel data avail- As the encoder-decoder framework deals with a sequence able to train models may not be enough to achieve high of tokens, a way to address the Thai language is to split the translation quality.
    [Show full text]
  • A Clustering-Based Algorithm for Automatic Document Separation
    A Clustering-Based Algorithm for Automatic Document Separation Kevyn Collins-Thompson Radoslav Nickolov School of Computer Science Microsoft Corporation Carnegie Mellon University 1 Microsoft Way 5000 Forbes Avenue Redmond, WA USA Pittsburgh, PA USA [email protected] [email protected] ABSTRACT For text, audio, video, and still images, a number of projects have addressed the problem of estimating inter-object similarity and the related problem of finding transition, or ‘segmentation’ points in a stream of objects of the same media type. There has been relatively little work in this area for document images, which are typically text-intensive and contain a mixture of layout, text-based, and image features. Beyond simple partitioning, the problem of clustering related page images is also important, especially for information retrieval problems such as document image searching and browsing. Motivated by this, we describe a model for estimating inter-page similarity in ordered collections of document images, based on a combination of text and layout features. The features are used as input to a discriminative classifier, whose output is used in a constrained clustering criterion. We do a task-based evaluation of our method by applying it the problem of automatic document separation during batch scanning. Using layout and page numbering features, our algorithm achieved a separation accuracy of 95.6% on the test collection. Keywords Document Separation, Image Similarity, Image Classification, Optical Character Recognition contiguous set of pages, but be scattered into several 1. INTRODUCTION disconnected, ordered subsets that we would like to recombine. Such scenarios are not uncommon when The problem of accurately determining similarity between scanning large volumes of paper: for example, one pages or documents arises in a number of settings when document may be accidentally inserted in the middle of building systems for managing document image another in the queue.
    [Show full text]
  • An Incremental Text Segmentation by Clustering Cohesion
    An Incremental Text Segmentation by Clustering Cohesion Raúl Abella Pérez and José Eladio Medina Pagola Advanced Technologies Application Centre (CENATAV), 7a #21812 e/ 218 y 222, Rpto. Siboney, Playa, C.P. 12200, Ciudad de la Habana, Cuba {rabella, jmedina} @cenatav.co.cu Abstract. This paper describes a new method, called IClustSeg, for linear text segmentation by topic using an incremental overlapped clustering algorithm. Incremental algorithms are able to process new objects as they are added to the collection and, according to the changes, to update the results using previous information. In our approach, we maintain a structure to get an incremental overlapped clustering. The results of the clustering algorithm, when processing a stream, are used any time text segmentation is required, using the clustering cohesion as the criteria for segmenting by topic. We compare our proposal against the best known methods, outperforming significantly these algorithms. 1 Introduction Topic segmentation intends to identify the boundaries in a document with goal of capturing the latent topical structure. The automatic detection of appropriate subtopic boundaries in a document is a very useful task in text processing. For example, in information retrieval and in passages retrieval, to return documents, segments or passages closer to the user’s queries. Another application of topic segmentation is in summarization, where it can be used to select segments of texts containing the main ideas for the summary requested [6]. Many text segmentation methods by topics have been proposed recently. Usually, they obtain linear segmentations, where the output is a document divided into sequences of adjacent segments [7], [9].
    [Show full text]
  • A Text Denormalization Algorithm Producing Training Data for Text Segmentation
    A Text Denormalization Algorithm Producing Training Data for Text Segmentation Kilian Evang Valerio Basile University of Groningen University of Groningen [email protected] [email protected] Johan Bos University of Groningen [email protected] As a first step of processing, text often has to be split into sentences and tokens. We call this process segmentation. It is often desirable to replace rule-based segmentation tools with statistical ones that can learn from examples provided by human annotators who fix the machine's mistakes. Such a statistical segmentation system is presented in Evang et al. (2013). As training data, the system requires the original raw text as well as information about the boundaries between tokens and sentences within this raw text. Although raw as well as segmented versions of text corpora are available for many languages, this required information is often not trivial to obtain because the segmented version differs from the raw one also in other respects. For example, punctuation marks and diacritics have been normalized to canonical forms by human annotators or by rule-based segmentation and normalization tools. This is the case with e.g. the Penn Treebank, the Dutch Twente News Corpus and the Italian PAISA` corpus. This problem of missing alignment between raw and segmented text is also noted by Dridan and Oepen (2012). We present a heuristic algorithm that recovers the alignment and thereby produces standoff annotations marking token and sentence boundaries in the raw test. The algorithm is based on the Levenshtein algorithm and is general in that it does not assume any language-specific normalization conventions.
    [Show full text]
  • Topic Segmentation: Algorithms and Applications
    University of Pennsylvania ScholarlyCommons IRCS Technical Reports Series Institute for Research in Cognitive Science 8-1-1998 Topic Segmentation: Algorithms And Applications Jeffrey C. Reynar University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/ircs_reports Part of the Databases and Information Systems Commons Reynar, Jeffrey C., "Topic Segmentation: Algorithms And Applications" (1998). IRCS Technical Reports Series. 66. https://repository.upenn.edu/ircs_reports/66 University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-98-21. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/ircs_reports/66 For more information, please contact [email protected]. Topic Segmentation: Algorithms And Applications Abstract Most documents are aboutmore than one subject, but the majority of natural language processing algorithms and information retrieval techniques implicitly assume that every document has just one topic. The work described herein is about clues which mark shifts to new topics, algorithms for identifying topic boundaries and the uses of such boundaries once identified. A number of topic shift indicators have been proposed in the literature. We review these features, suggest several new ones and test most of them in implemented topic segmentation algorithms. Hints about topic boundaries include repetitions of character sequences, patterns of word and word n-gram repetition, word frequency, the presence of cue words and phrases and the use of synonyms. The algorithms we present use cues singly or in combination to identify topic shifts in several kinds of documents. One algorithm tracks compression performance, which is an indicator of topic shift because self-similarity within topic segments should be greater than between-segment similarity.
    [Show full text]
  • A Generic Neural Text Segmentation Model with Pointer Network
    Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) SEGBOT: A Generic Neural Text Segmentation Model with Pointer Network Jing Li, Aixin Sun and Shafiq Joty School of Computer Science and Engineering, Nanyang Technological University, Singapore [email protected], {axsun,srjoty}@ntu.edu.sg Abstract [A person]EDU [who never made a mistake]EDU [never tried any- thing new]EDU Text segmentation is a fundamental task in natu- ral language processing that comes in two levels of Figure 1: A sentence with three elementary discourse units (EDUs). granularity: (i) segmenting a document into a se- and Thompson, 1988]. quence of topical segments (topic segmentation), Both topic and EDU segmentation tasks have received a and (ii) segmenting a sentence into a sequence of lot of attention in the past due to their utility in many NLP elementary discourse units (EDU segmentation). tasks. Although related, these two tasks have been addressed Traditional solutions to the two tasks heavily rely separately with different sets of approaches. Both supervised on carefully designed features. The recently propo- and unsupervised methods have been proposed for topic seg- sed neural models do not need manual feature en- mentation. Unsupervised topic segmentation models exploit gineering, but they either suffer from sparse boun- the strong correlation between topic and lexical usage, and dary tags or they cannot well handle the issue of can be broadly categorized into two classes: similarity-based variable size output vocabulary. We propose a ge- models and probabilistic generative models. The similarity- neric end-to-end segmentation model called SEG- based models are based on the key intuition that sentences BOT.SEGBOT uses a bidirectional recurrent neural in a segment are more similar to each other than to senten- network to encode input text sequence.
    [Show full text]
  • Text Segmentation Techniques: a Critical Review
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Sunway Institutional Repository Text Segmentation Techniques: A Critical Review Irina Pak and Phoey Lee Teh Department of Computing and Information Systems, Sunway University, Bandar Sunway, Malaysia [email protected], [email protected] Abstract Text segmentation is widely used for processing text. It is a method of splitting a document into smaller parts, which is usually called segments. Each segment has its relevant meaning. Those segments categorized as word, sentence, topic, phrase or any information unit depending on the task of the text analysis. This study presents various reasons of usage of text segmentation for different analyzing approaches. We categorized the types of documents and languages used. The main contribution of this study includes a summarization of 50 research papers and an illustration of past decade (January 2007- January 2017)’s of research that applied text segmentation as their main approach for analysing text. Results revealed the popularity of using text segmentation in different languages. Besides that, the “word” seems to be the most practical and usable segment, as it is the smaller unit than the phrase, sentence or line. 1 Introduction Text segmentation is process of extracting coherent blocks of text [1]. The segment referred as “segment boundary” [2] or passage [3]. Another two studies referred segment as subtopic [4] and region of interest [5]. There are many reasons why the splitting document can be useful for text analysis. One of the main reasons is because they are smaller and more coherent than whole documents [3].
    [Show full text]
  • Text Segmentation Based on Semantic Word Embeddings
    Text Segmentation based on Semantic Word Embeddings Alexander A Alemi Paul Ginsparg Dept of Physics Depts of Physics and Information Science Cornell University Cornell University [email protected] [email protected] ABSTRACT early algorithm in this class was Choi's C99 algorithm [3] We explore the use of semantic word embeddings [14, 16, in 2000, which also introduced a benchmark segmentation 12] in text segmentation algorithms, including the C99 seg- dataset used by subsequent work. Instead of looking only at nearest neighbor coherence, the C99 algorithm computes mentation algorithm [3, 4] and new algorithms inspired by 1 the distributed word vector representation. By developing a coherence score between all pairs of elements of text, a general framework for discussing a class of segmentation and searches for a text segmentation that optimizes an ob- objectives, we study the effectiveness of greedy versus ex- jective based on that scoring by greedily making a succes- act optimization approaches and suggest a new iterative re- sion of best cuts. Later work by Choi and collaborators finement technique for improving the performance of greedy [4] used distributed representations of words rather than a strategies. We compare our results to known benchmarks bag of words approach, with the representations generated [18, 15, 3, 4], using known metrics [2, 17]. We demonstrate by LSA [8]. In 2001, Utiyama and Ishahara introduced a state-of-the-art performance for an untrained method with statistical model for segmentation and optimized a poste- our Content Vector Segmentation (CVS) on the Choi test rior for the segment boundaries. Moving beyond the greedy set.
    [Show full text]
  • Steps Involved in Text Recognition and Recent Research in OCR; a Study
    International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1, May 2019 Steps Involved in Text Recognition and Recent Research in OCR; A Study K.Karthick, K.B.Ravindrakumar, R.Francis, S.Ilankannan Abstract: The Optical Character Recognition (OCR) is one of Preprocessing the automatic identification techniques that fulfill the automation Feature Extraction needs in various applications. A machine can read the Recognition information present in natural scenes or other materials in any Post processing form with OCR. The typed and printed character recognition is uncomplicated due to its well-defined size and shape. The In addition to the above steps segmentation and handwriting of individuals differs in the above aspects. So, the morphological processing also involved in the recognition handwritten OCR system faces complexity to learn this difference process. These steps may be added before the feature to recognize a character. In this paper, we discussed the various extraction process. stages in text recognition, handwritten OCR systems classification according to the text type, study on Chinese and II. PREPROCESSING Arabic text recognition as well as application oriented recent research in OCR. The preprocessing is a fundamental stage that is proceeding Index Terms: Edge detection, Optical Character Recognition, OCR, Preprocessing stages, Text Recognition to the stage of feature extraction; it regulates the appropriateness of the outcomes for the consecutive stages. I. INTRODUCTION The OCR success rate is contingent on the success percentage of each stage. The technologyadventfoundan amazing and noble development curve for the last two centuries. For the last A. Factors Affecting the Text Recognition Quality few decades, it is easy in using mouse and keyboard to assist as interfacing device between us and computer.
    [Show full text]
  • Word Sense Disambiguation and Text Segmentation Based on Lexical
    Word Sense i)ismnl}iguati<)n and Text Set mentation Bas(q on I,c×ical (+ohcslOll ()KUMUI{A Manabu, IIONI)A Takeo School of [nforma,tion Science, Japan Advanced Institute of Science a.nd Technology ('l'al.sunokuchi, lshikawa 923-12 Japan) c-nmil: { oku,honda}¢~jaist.ac.ji~ Abstract cohesion is far easier to idenlAfy than reference be- cause 1)oth words in lexical cohesion relation ap- In this paper, we describe ihow word sense am= pear in a text while one word in reference relation biguity can be resolw'.d with the aid of lexical eo- is a pr<mom, or elided and has less information to hesion. By checking ]exical coheshm between the infer the other word in the relation automatically. current word and lexical chains in the order of Based on this observation, we use lexical cohe- the salience, in tandem with getmration of lexica] sion as a linguistic device for discourse analysis. chains~ we realize incretnental word sense disam We call a sequence of words which are in lexieal biguation based on contextual infl)rmation that cohesion relation with each other a Icxical chain lexical chains,reveah Next;, we <le~<:ribe how set like [10]. l,exical chains tend to indicate portions men< boundaries of a text can be determined with of a text; that form a semantic uttit. And so vari.- the aid of lexical cohesion. Wc can measure the ous lexical chains tend to appear in a text corre. plausibility of each point in the text as a segment spou(ling to the change of the topic.
    [Show full text]
  • Multiple Segmentations of Thai Sentences for Neural Machine
    Multiple Segmentations of Thai Sentences for Neural Machine Translation Alberto Poncelas1, Wichaya Pidchamook2, Chao-Hong Liu3, James Hadley4, Andy Way1 1ADAPT Centre, School of Computing, Dublin City University, Ireland 2SALIS, Dublin City University, Ireland 3Iconic Translation Machines 4Trinity Centre for Literary and Cultural Translation, Trinity College Dublin, Ireland {alberto.poncelas, andy.way}@adaptcentre.ie [email protected], [email protected], [email protected] Abstract Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool. Keywords: Machine Translation, Word Segmentation, Thai Language In Machine Translation (MT), low-resource languages are sentence into words (or tokens). We investigate three split- especially challenging as the amount of parallel data avail- ting strategies: (i) Character-based, using each character as able to train models may not be enough to achieve high token. This is the simplest approach as there is no need to translation quality.
    [Show full text]