Dissertations / Theses on the topic 'Semantics of word-forming base'
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Sinha, Ravi Som. "Graph-based Centrality Algorithms for Unsupervised Word Sense Disambiguation." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc9736/.
Full textGrover, Ishaan. "A semantics based computational model for word learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120694.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 73-77).
Studies have shown that children's early literacy skills can impact their ability to achieve academic success, attain higher education and secure employment later in life. However, lack of resources and limited access to educational content causes a "knowledge gap" between children that come from different socio-economic backgrounds. To solve this problem, there has been a recent surge in the development of Intelligent Tutoring Systems (ITS) to provide learning benefits to children. However, before providing new content, an ITS must assess a child's existing knowledge. Several studies have shown that children learn new words by forming semantic relationships with words they already know. Human tutors often implicitly use semantics to assess a tutee's word knowledge from partial and noisy data. In this thesis, I present a cognitively inspired model that uses word semantics (semantics-based model) to make inferences about a child's vocabulary from partial information about their existing vocabulary. Using data from a one-to-one learning intervention between a robotic tutor and 59 children, I show that the proposed semantics-based model outperforms (on average) models that do not use word semantics (semantics-free models). A subject level analysis of results reveals that different models perform well for different children, thus motivating the need to combine predictions. To this end, I present two methods to combine predictions from semantics-based and semantics-free models and show that these methods yield better predictions of a child's vocabulary knowledge. Finally, I present an application of the semantics-based model to evaluate if a learning intervention was successful in teaching children new words while enhancing their semantic understanding. More concretely, I show that a personalized word learning intervention with a robotic tutor is better suited to enhance children's vocabulary when compared to a non-personalized intervention. These results motivate the use of semantics-based models to assess children's knowledge and build ITS that maximize children's semantic understanding of words.
"This research was supported by NSF IIP-1717362 and NSF IIS-1523118"--Page 10.
by Ishaan Grover.
S.M.
Burton, Marilyn Elizabeth. "Semantics of glory : a cognitive, corpus-based approach to Hebrew word meaning." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9573.
Full textSinha, Ravi Som Mihalcea Rada F. "Graph-based centrality algorithms for unsupervised word sense disambiguation." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-9736.
Full textEsin, Yunus Emre. "Improvement Of Corpus-based Semantic Word Similarity Using Vector Space Model." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610759/index.pdf.
Full textManion, Steve Lawrence. "Unsupervised Knowledge-based Word Sense Disambiguation: Exploration & Evaluation of Semantic Subgraphs." Thesis, University of Canterbury. Department of Mathematics & Statistics, 2014. http://hdl.handle.net/10092/10016.
Full textLilliehöök, Hampus. "Extraction of word senses from bilingual resources using graph-based semantic mirroring." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-91880.
Full textI det här arbetet utvinner vi semantisk information som existerar implicit i tvåspråkig data. Vi samlar indata genom att upprepa proceduren semantisk spegling. Datan representeras som vektorer i en stor vektorrymd. Vi bygger sedan en resurs med synonymkluster genom att applicera K-means-algoritmen på vektorerna. Vi granskar resultatet för hand med hjälp av ordböcker, och mot WordNet, och diskuterar möjligheter och tillämpningar för metoden.
Milajevs, Dmitrijs. "A study of model parameters for scaling up word to sentence similarity tasks in distributional semantics." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/36225.
Full textIslam, Md Aminul. "Applications of corpus-based semantic similarity and word segmentation to database schema matching." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27256.
Full textStigeborn, Olivia. "Text ranking based on semantic meaning of sentences." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300442.
Full textAtt hitta en lämplig kandidat till kundmatchning är en viktig del av ett konsultföretags arbete. Det tar mycket tid och ansträngning för rekryterare på företaget att läsa eventuellt hundratals CV:n för att hitta en lämplig kandidat. Det finns språkteknologiska metoder för att rangordna CV:n med de mest lämpliga kandidaterna rankade högst. Detta säkerställer att rekryterare endast behöver titta på de topprankade CV:erna och snabbt kan få kandidater ut i fältet. Tidigare forskning har använt metoder som räknar specifika nyckelord i ett CV och är kapabla att avgöra om en kandidat har specifika erfarenheter. Huvudmålet med denna avhandling är att använda den semantiska innebörden av texten iCV:n för att få en djupare förståelse för en kandidats erfarenhetsnivå. Den utvärderar också om modellen kan köras på mobila enheter och om algoritmen kan rangordna CV:n oberoende av om CV:erna är på svenska eller engelska. En algoritm skapades som använder ordinbäddningsmodellen DistilRoBERTa som är kapabel att fånga textens semantiska betydelse. Algoritmen utvärderades genom att generera jobbeskrivningar från CV:n genom att skapa en sammanfattning av varje CV. Körtiden, minnesanvändningen och rankningen som den önskade kandidaten fick dokumenterades och användes för att analysera resultatet. När den kandidat som användes för att generera jobbeskrivningen rankades i topp 10 ansågs klassificeringen vara korrekt. Noggrannheten beräknades med denna metod och en noggrannhet på 68,3 % uppnåddes. Resultaten visar att algoritmen kan rangordna CV:n. Algoritmen kan rangordna både svenska och engelska CV:n med en noggrannhet på 67,7 % för svenska och 74,7 % för engelska. Körtiden var i genomsnitt 578 ms vilket skulle möjliggöra att algoritmen kan köras på mobila enheter men minnesanvändningen var för stor. Sammanfattningsvis kan den semantiska betydelsen av CV:n användas för att rangordna CV:n och ett eventuellt framtida arbete är att kombinera denna metod med en metod som räknar nyckelord för att undersöka hur noggrannheten skulle påverkas.
Matikainen, Tiina Johanna. "Semantic Representation of L2 Lexicon in Japanese University Students." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/133319.
Full textEd.D.
In a series of studies using semantic relatedness judgment response times, Jiang (2000, 2002, 2004a) has claimed that L2 lexical entries fossilize with their equivalent L1 content or something very close to it. In another study using a more productive test of lexical knowledge (Jiang 2004b), however, the evidence for this conclusion was less clear. The present study is a partial replication of Jiang (2004b) with Japanese learners of English. The aims of the study are to investigate the influence of the first language (L1) on second language (L2) lexical knowledge, to investigate whether lexical knowledge displays frequency-related, emergent properties, and to investigate the influence of the L1 on the acquisition of L2 word pairs that have a common L1 equivalent. Data from a sentence completion task was completed by 244 participants, who were shown sentence contexts in which they chose between L2 word pairs sharing a common equivalent in the students' first language, Japanese. The data were analyzed using the statistical analyses available in the programming environment R to quantify the participants' ability to discriminate between synonymous and non-synonymous use of these L2 word pairs. The results showed a strong bias against synonymy for all word pairs; the participants tended to make a distinction between the two synonymous items by assigning each word a distinct meaning. With the non-synonymous items, lemma frequency was closely related to the participants' success in choosing the correct word in the word pair. In addition, lemma frequency and the degree of similarity between the words in the word pair were closely related to the participants' overall knowledge of the non-synonymous meanings of the vocabulary items. The results suggest that the participants had a stronger preference for non-synonymous options than for the synonymous option. This suggests that the learners might have adopted a one-word, one-meaning learning strategy (Willis, 1998). The reasonably strong relationship between several of the usage-based statistics and the item measures from R suggest that with exposure learners are better able to use words in ways that are similar to native speakers of English, to differentiate between appropriate and inappropriate contexts and to recognize the boundary separating semantic overlap and semantic uniqueness. Lexical similarity appears to play a secondary role, in combination with frequency, in learners' ability to differentiate between appropriate and inappropriate contexts when using L2 word pairs that have a single translation in the L1.
Temple University--Theses
Konduri, Aparna. "CLustering of Web Services Based on Semantic Similarity." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1199657471.
Full textWang, Qianqian. "NATURAL LANGUAGE PROCESSING BASED GENERATOR OF TESTING INSTRUMENTS." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/576.
Full textPierrejean, Bénédicte. "Qualitative evaluation of word embeddings : investigating the instability in neural-based models." Thesis, Toulouse 2, 2020. http://www.theses.fr/2020TOU20001.
Full textDistributional semantics has been revolutionized by neural-based word embeddings methods such as word2vec that made semantics models more accessible by providing fast, efficient and easy to use training methods. These dense representations of lexical units based on the unsupervised analysis of large corpora are more and more used in various types of applications. They are integrated as the input layer in deep learning models or they are used to draw qualitative conclusions in corpus linguistics. However, despite their popularity, there still exists no satisfying evaluation method for word embeddings that provides a global yet precise vision of the differences between models. In this PhD thesis, we propose a methodology to qualitatively evaluate word embeddings and provide a comprehensive study of models trained using word2vec. In the first part of this thesis, we give an overview of distributional semantics evolution and review the different methods that are currently used to evaluate word embeddings. We then identify the limits of the existing methods and propose to evaluate word embeddings using a different approach based on the variation of nearest neighbors. We experiment with the proposed method by evaluating models trained with different parameters or on different corpora. Because of the non-deterministic nature of neural-based methods, we acknowledge the limits of this approach and consider the problem of nearest neighbors instability in word embeddings models. Rather than avoiding this problem we embrace it and use it as a mean to better understand word embeddings. We show that the instability problem does not impact all words in the same way and that several linguistic features are correlated. This is a step towards a better understanding of vector-based semantic models
Al, Tayyar Musaid Seleh. "Arabic information retrieval system based on morphological analysis (AIRSMA) : a comparative study of word, stem, root and morpho-semantic methods." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/4126.
Full textPellén, Angelica. "Oh foxy lady, where art thou? : A corpus based analysis of the word foxy, from a gender stereotype perspective." Thesis, Växjö University, School of Humanities, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-2569.
Full textAbstract
The aim of this essay is to establish whether or not the word foxy can serve to illustrate gender differences and gender stereotypes in English. The analysis is conducted by using one American English corpus and one British English corpus in order to make a comparison of the two English varieties. Apart from the comparative study, foxy is examined and categorized according to gender and a number of features to help answering the research questions which are:
• What difference in meaning, if any, does the word foxy carry when used for males, females and inanimate things?
• Can the word foxy serve to illustrate gender stereotypes in English?
• Are there any differences regarding how foxy is used in American English compared to British English?
Throughout the essay previous studies are presented, terms and tools that have been used are defined and argued for. One of the conclusions drawn in this study is that there is a significant difference in meaning when foxy is used in American English compared to British English. There are, however, also differences concerning the use of foxy when referring to males, females and inanimate things.
Keywords: Collocation, corpus studies, foxy, gender, language, linguistics, semantic prosody, stereotypes.
Dergachyova, Olga. "Knowledge-based support for surgical workflow analysis and recognition." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S059/document.
Full textComputer assistance became indispensable part of modern surgical procedures. Desire of creating new generation of intelligent operating rooms incited researchers to explore problems of automatic perception and understanding of surgical situations. Situation awareness includes automatic recognition of surgical workflow. A great progress was achieved in recognition of surgical phases and gestures. Yet, there is still a blank between these two granularity levels in the hierarchy of surgical process. Very few research is focused on surgical activities carrying important semantic information vital for situation understanding. Two important factors impede the progress. First, automatic recognition and prediction of surgical activities is a highly challenging task due to short duration of activities, their great number and a very complex workflow with multitude of possible execution and sequencing ways. Secondly, very limited amount of clinical data provides not enough information for successful learning and accurate recognition. In our opinion, before recognizing surgical activities a careful analysis of elements that compose activity is necessary in order to chose right signals and sensors that will facilitate recognition. We used a deep learning approach to assess the impact of different semantic elements of activity on its recognition. Through an in-depth study we determined a minimal set of elements sufficient for an accurate recognition. Information about operated anatomical structure and surgical instrument was shown to be the most important. We also addressed the problem of data deficiency proposing methods for transfer of knowledge from other domains or surgeries. The methods of word embedding and transfer learning were proposed. They demonstrated their effectiveness on the task of next activity prediction offering 22% increase in accuracy. In addition, pertinent observations about the surgical practice were made during the study. In this work, we also addressed the problem of insufficient and improper validation of recognition methods. We proposed new validation metrics and approaches for assessing the performance that connect methods to targeted applications and better characterize capacities of the method. The work described in this these aims at clearing obstacles blocking the progress of the domain and proposes a new perspective on the problem of surgical workflow recognition
Utgof, Darja. "The Perception of Lexical Similarities Between L2 English and L3 Swedish." Thesis, Linköping University, Department of Culture and Communication, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15874.
Full textThe present study investigates lexical similarity perceptions by students of Swedish as a foreign language (L3) with a good yet non-native proficiency in English (L2). The general theoretical framework is provided by studies in transfer of learning and its specific instance, transfer in language acquisition.
It is accepted as true that all previous linguistic knowledge is facilitative in developing proficiency in a new language. However, a frequently reported phenomenon is that students see similarities between two systems in a different way than linguists and theoreticians of education do. As a consequence, the full facilitative potential of transfer remains unused.
The present research seeks to shed light on the similarity perceptions with the focus on the comprehension of a written text. In order to elucidate students’ views, a form involving similarity judgements and multiple choice questions for formally similar items has been designed, drawing on real language use as provided by corpora. 123 forms have been distributed in 6 groups of international students, 4 of them studying Swedish at Level I and 2 studying at Level II.
The test items in the form vary in the degree of formal, semantic and functional similarity from very close cognates, to similar words belonging to different word classes, to items exhibiting category membership and/or being in subordinate/superordinate relation to each other, to deceptive cognates. The author proposes expected similarity ratings and compares them to the results obtained. The objective measure of formal similarity is provided by a string matching algorithm, Levenshtein distance.
The similarity judgements point at the fact that intermediate similarity values can be considered problematic. Similarity ratings between somewhat similar items are usually lower than could be expected. Besides, difference in grammatical meaning lowers similarity values significantly even if lexical meaning nearly coincides. Thus, the obtained results indicate that in order to utilize similarities to facilitate language learning, more attention should be paid to underlying similarities.
Chang, Chia-Yang, and 張家揚. "Plagiarism detection based on word semantic clustering." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3w54sj.
Full text國立中山大學
電機工程學系研究所
106
Plagiarism is a common problem in current years. With the advance of Internet, it is more and more easy to obtain other people''s writings. When someone uses the content without citation, he may cause the problem of plagiarism. Plagiarisms will infringe the intellectual property rights. So plagiarism detection is a serious problem in nowadays.Current plagiarism detection methods are similar to near-duplicate detection methods, like VSM(vector space model) or bag-of-words. These methods can''t handle the complex plagiarized technique very well, e.g. word substitution and sentence rewriting. Therefore, we focus on the semantic of words. In this paper, we propose a new method for plagiarism detection by analyzing the semantic of words.Word2vec is a word embedding model proposed by Google group. It can use a vector to represent a word. We use Word2vec to obtain the vector of words and use PCA for dimension reduction. After that, we use spherical K-means to cluster the words into concepts. By using Word2vec, we can consider the semantic of words and cluster the words into concepts in order to deal with the complex plagiarized technique.Finally, we will show our experimental results and compare with other methods. The experimental results show that our method is well performance.
Mohammad, Saif. "Measuring Semantic Distance using Distributional Profiles of Concepts." Thesis, 2008. http://hdl.handle.net/1807/11238.
Full textChen, Hsiao-Yi, and 陳曉毅. "A Semantic Search over Encrypted Cloud Data based on Word Embedding 研." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7b4m86.
Full text國立臺灣科技大學
資訊工程系
107
The services of cloud storage have been very popular in recent years. With the superiority of low-cost and high-capacity, people are inclined to move their data from a local computer to a remote facility such as the cloud server. The majority of the existing methods for searching data on the cloud concentrate on keyword-based search scheme. With the rise of information security awareness, data owners hope that the data placed in the cloud server can keep privacy from being snooped by untrusted users, and users also hope that their query content will not be record by untrusted server. Therefore, encrypting data and queries is the most common way.However, the encrypted ciphertext has lost the relationship of the original plaintext, which will cause many difficulties in keyword search.In addition, most of the existing search methods are not able to efficiently obtain the information that the user is really interested in from the user's query keywords. To address these problems, this study proposes a word embedding based semantic search scheme for searching documents on the cloud. The word embedding model is implemented by a neural network. The neural network model can learn the semantic relationship between words in the corpus and express the words in vectors. By using a word-embedded model, a document index vector and a query vector can be generated. The proposed scheme can encrypt the query vector and the index vector into ciphertext, which can preserve the efficiency of the search while protecting the privacy of the user and the security of the document.