DOCSLIB.ORG

Improving Word Segmentation by Simultaneously Learning Phonotactics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Improving Word Segmentation by Simultaneously Learning Phonotactics Improving Word Segmentation by Simultaneously Learning Phonotactics Daniel Blanchard Jeffrey Heinz Computer & Information Sciences Linguistics & Cognitive Science University of Delaware University of Delaware [email protected] [email protected] Abstract six months infants can begin to segment words out of speech (Bortfeld et al., 2005). Here we present The most accurate unsupervised word seg- an efficient word segmentation system aimed to mentation systems that are currently avail- model how infants accomplish the task. able (Brent, 1999; Venkataraman, 2001; While an algorithm that could reliably extract Goldwater, 2007) use a simple unigram orthographic representations of both novel and fa- model of phonotactics. While this sim- miliar words from acoustic data is something we plifies some of the calculations, it over- would like to see developed, following earlier re- looks cues that infant language acquisition searchers, we simplify the problem by using a text researchers have shown to be useful for that does not contain any word boundary markers. segmentation (Mattys et al., 1999; Mattys Hereafter, we use the phrase “word segmentation” and Jusczyk, 2001). Here we explore the to mean some process which adds word boundaries utility of using bigram and trigram phono- to a text that does not contain them. tactic models by enhancing Brent’s (1999) This paper’s focus is on unsupervised, incre- MBDP-1 algorithm. The results show mental word segmentation algorithms; i.e., those the improved MBDP-Phon model outper- that do not rely on preexisting knowledge of a par- forms other unsupervised word segmenta- ticular language, and those that segment the cor- tion systems (e.g., Brent, 1999; Venkatara- pus one utterance at a time. This is in contrast man, 2001; Goldwater, 2007). to supervised word segmentation algorithms (e.g., 1 Introduction Teahan et al., 2000), which are typically used for segmenting text in documents written in languages How do infants come to identify words in the that do not put spaces between their words like speech stream? As adults, we break up speech Chinese. (Of course, unsupervised word segmen- into words with such ease that we often think tation algorithms also have this application.) This that there are audible pauses between words in the also differs from batch segmentation algorithms same sentence. However, unlike some written lan- (Goldwater, 2007; Johnson, 2008b; Fleck, 2008), guages, speech does not have any completely reli- which process the entire corpus at least once be- able markers for the breaks between words (Cole fore outputting a segmentation of the corpus. Un- and Jakimik, 1980). In fact, languages vary on how supervised incremental algorithms are of interest they signal the ends of words (Cutler and Carter, to some psycholinguists and acquisitionists inter- 1987), which makes the task even more daunting. ested in the problem of language learning, as well Adults at least have a lexicon they can use to rec- as theoretical computer scientists who are inter- ognize familiar words, but when an infant is first ested in what unsupervised, incremental models born, they do not have a pre-existing lexicon to are capable of achieving. consult. In spite of these challenges, by the age of Phonotactic patterns are the rules that deter- c 2008. Licensed under the Creative Commons mine what sequences of phonemes or allophones Attribution-Noncommercial-Share Alike 3.0 Unported li- cense (http://creativecommons.org/licenses/by-nc-sa/3.0/). are allowable within words. Learning the phono- Some rights reserved. tactic patterns of a language is usually modeled 65 CoNLL 2008: Proceedings of the 12th Conference on Computational Natural Language Learning, pages 65–72 Manchester, August 2008 separately from word segmentation; e.g., current 5. Concatenate the words in the order specified phonotactic learners such as Coleman and Pierre- by s, and remove the word delimiters (#). humbert (1997), Heinz (2007), or Hayes and Wil- It is important to note that this model treats the son (2008) are given word-sized units as input. generation of the text as a single event in the prob- However, infants appear to simultaneously learn ability space, which allows Brent to make a num- which phoneme combinations are allowable within ber of simplifying assumptions. As the values for words and how to extract words from the input. It n, L, f, and s completely determine the segmenta- is reasonable that the two processes feed into one tion, the probability of a particular segmentation, another, and when infants acquire a critical mass of wm, can be calculated as: phonotactic knowledge, they use it to make judge- ments about what phoneme sequences can occur P (wm) = P (n, L, f, s) (1) within versus across word boundaries (Mattys and To allow the model to operate on one utterance at Jusczyk, 2001). We use this insight, also suggested a time, Brent states the probability of each word in by Venkataraman (2001) and recently utilized by the text as a recursive function, R(w ), where w Fleck (2008) in a different manner, to enhance k k is the text up to and including the word at position Brent’s (1999) model MBDP-1, and significantly k, w . Furthermore, there are two specific cases increase segmentation accuracy. We call this mod- k for R: familiar words and novel words. If w is ified segmentation model MBDP-Phon. k familiar, the model already has the word in its lex- 2 Related Work icon, and its score is calculated as in Equation 2. 2 f(wk) f(wk) 1 2.1 Word Segmentation R(wk) = − (2) k · f(wk) The problem of unsupervised word segmentation has attracted many earlier researchers over the Otherwise, the word is novel, and its score is cal- 1 past fifty years (e.g., Harris, 1954; Olivier, 1968; culated using Equation 3 (Brent and Tao, 2001), de Marcken, 1995; Brent, 1999). In this section, R(wk) = we describe the base model MBDP-1, along with 2 6 n PΣ(a1)...PΣ(aq) n 1 (3) π2 k 1 P (#) −n two other segmentation approaches, Venkataraman · · − Σ · (2001) and Goldwater (2007). In 4, we compare § where PΣ is the probability of a particular MBDP-Phon to these models in more detail. For phoneme occurring in the text. The third term of a thorough review of word segmentation literature, the equation for novel words is where the model’s see Brent (1999) or Goldwater (2007). unigram phonotactic model comes into play. We detail how to plug a more sophisticated phonotac- 2.1.1 MBDP-1 tic learning model into this equation in 3. With § Brent’s (1999) MBDP-1 (Model Based Dy- the generative model established, MBDP-1 uses a namic Programming) algorithm is an implemen- Viterbi-style search algorithm to find the segmen- tation of the INCDROP framework (Brent, 1997) tation for each utterance that maximizes the R val- that uses a Bayesian model of how to generate an ues for each word in the segmentation. unsegmented text to insert word boundaries. The Venkataraman (2001) notes that considering the generative model consists of five steps: generation of the text as a single event is un- 1. Choose a number of word types, n. likely to be how infants approach the segmenta- tion problem. However, MBDP-1 uses an incre- 2. Pick n distinct strings from Σ+#, which will mental search algorithm to segment one utterance make up the lexicon, L. Entries in L are la- at a time, which is more plausible as a model of beled W1 ...Wn. W0 = $, where $ is the infants’ word segmentation. utterance boundary marker. 1Brent (1999) originally described the novel word score 2 6 n Pσ (Wn ) n 1 k k k− as R(wk) = π2 k nk 1 nk n , · · 1 − Pσ (Wj ) · k 3. Pick a function, f, which maps word types to − nk · j=1 their frequency in the text. where Pσ is the probability of all the phonemes in the word occurring together, but the denominatorP of the third term was dropped in Brent and Tao (2001). This change drastically 4. Choose a function, s, to map positions in the speeds up the model, and only reduces segmentation accuracy text to word types. by 0.5%. ∼ 66 2.1.2 Venkataraman (2001) humbert, 1997; Heinz, 2007; Hayes and Wilson, MBDP-1 is not the only incremental unsuper- 2008). While Hayes and Wilson present a more vised segmentation model that achieves promis- complex Maximum Entropy phonotactic model in ing results. Venkataraman’s (2001) model tracks their paper than the one we add to MBDP-1, they MBDP-1’s performance so closely that Batchelder also evaluate a simple n-gram phonotactic learner (2002) posits that the models are performing the operating over phonemes. The input to the mod- same operations, even though the authors describe els is a list of English onsets and their frequency them differently. in the lexicon, and the basic trigram learner simply Venkataraman’s model uses a more traditional, keeps track of the trigrams it has seen in the cor- smoothed n-gram model to describe the distribu- pus. They test the model on novel words with ac- tion of words in an unsegmented text.2 The most ceptable rhymes—some well-formed (e.g., [kIp]), probable segmentation is retrieved via a dynamic and some less well-formed (e.g., [stwIk])—so any programming algorithm, much like Brent (1999). ill-formedness is attributable to onsets. This ba- We use MBDP-1 rather than Venkataraman’s sic trigram model explains 87.7% of the variance approach as the basis for our model only because it in the scores that Scholes (1966) reports his 7th was more transparent how to plug in a phonotactic grade students gave when subjected to the same learning module at the time this project began.
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]