Semantic similarity

Product manuals have been virtually ignored in published research despite their relevance to consumer satisfaction and marketplace safety. This study examines self-efficacy (perceptions of capabilities related to operating a product) and how it influences the effectiveness of and is also influenced by three safety message formats commonly used in product manuals (i.e., 1. safety messages presented in list format at

Resolving semantic heterogeneity across distinct data sources remains a highly relevant problem in the GIS domain requiring innovative solutions. Our approach, called GSim, semantically aligns tables from respective GIS databases by first choosing attributes for comparison. We then examine their instances and calculate a similarity value between them called entropy-based distribution (EBD) 1 by combining two separate methods. Our primary method discerns the geographic types from instances of compared attributes. If successful, EBD is calculated using only this method. GSim further facilitates geographic type matching by using latlong values to further disambiguate between multiple types of a given instance and applying attribute weighting to quantify the uniqueness of mapped attributes. If geographic type matching is not possible, we then apply a generic schema matching method, independent of the knowledge domain, which employs normalized Google distance. We show the effectiveness of our approach over the traditional approaches across multi-jurisdictional datasets by generating impressive results.

A prevailing neurobiological theory of semantic memory proposes that part of our knowledge about concrete, highly imageable concepts is stored in the form of sensory -motor representations. While this theory predicts differential activation of the semantic system by concrete and abstract words, previous functional imaging studies employing this contrast have provided relatively little supporting evidence. We acquired event-related functional magnetic resonance imaging (fMRI) data while participants performed a semantic similarity judgment task on a large number of concrete and abstract noun triads. Task difficulty was manipulated by varying the degree to which the words in the triad were similar in meaning. Concrete nouns, relative to abstract nouns, produced greater activation in a bilateral network of multimodal and heteromodal association areas, including ventral and medial temporal, posterior -inferior parietal, dorsal prefrontal, and posterior cingulate cortex. In contrast, abstract nouns produced greater activation almost exclusively in the left hemisphere in superior temporal and inferior frontal cortex. Increasing task difficulty modulated activation mainly in attention, working memory, and response monitoring systems, with almost no effect on areas that were modulated by imageability. These data provide critical support for the hypothesis that concrete, imageable concepts activate perceptually based representations not available to abstract concepts. In contrast, processing abstract concepts makes greater demands on left perisylvian phonological and lexical retrieval systems. The findings are compatible with dual coding theory and less consistent with single-code models of conceptual representation. The lack of overlap between imageability and task difficulty effects suggests that once the neural representation of a concept is activated, further maintenance and manipulation of that information in working memory does not further increase neural activation in the conceptual store. D

In this paper, we propose the Normalized Freebase Distance (NFD), a new measure for determing semantic concept relatedness that is based on similar principles as the Normalized Web Distance (NWD). We illustrate that the NFD is more effective when comparing ambiguous concepts.

A number of computational measures for determining semantic similarity between pairs of biomedical concepts have been developed using various standards and programming platforms. In this paper, we introduce two new open-source frameworks based on the Unified Medical Language System (UMLS). These frameworks consist of the UMLS-Similarity and UMLS-Interface packages. UMLS-Interface provides path information about UMLS concepts. UMLS-Similarity calculates the semantic similarity between UMLS concepts using several previously developed measures and can be extended to include new measures. We validate the functionality of these frameworks by reproducing the results from previous work. Our frameworks constitute a significant contribution to the field of biomedical Natural Language Processing by providing a common development and testing platform for semantic similarity measures based on the UMLS.

This study examines how the cross-linguistic similarity of translation equivalents affects bilingual word recognition. Performing one of three tasks, Dutch-English bilinguals processed cognates with varying degrees of form overlap between their English and Dutch counterparts (e.g., lamp-lamp vs. flood-vloed vs. song-lied). In lexical decision, reaction times decreased going from translation equivalents without any cross-linguistic orthographic overlap to very similar but non-identical cognates. Identical cognates showed a large discontinuous processing advantage and were subject to facilitation from phonological similarity. In language decision, the effect of orthographic similarity reversed: A cognate inhibition effect arose, the size of which increased with orthographic similarity. Here identical cognates were markedly slower than other cognates. In progressive demasking, no orthographic similarity effect was found for non-identical cognates, but a semantic similarity effect arose. In addition, there was a facilitation effect for identical cognates of low English frequency. The task-dependent result patterns are interpreted in terms of four accounts of cognate representation and provide evidence in favor of a localist connectionist account.

This research investigates the effects of syntactic, semantic, and morphophonemic similarity in the processing of center-embedding constructions in Japanese and Korean. Six Japanese experiments study the effects of syntactic and semantic similarity, while one Korean experiment deals with the effects of syntactic and morphophonemic similarity. The results from these experiments support the view that the difficulty of processing center-embeddings is

Document (text) classification is a common method in e-business, facilitating users in the tasks such as document collection, analysis, categorization and storage. Semantic analysis can help to improve the performance of document classification. Though having been considered when designing previous methods for automatic document classification, more focus should be given to semantics with the increase number of content-rich electronic documents, forum posts or blogs online, which can reduce human workload by a great margin. This paper proposes a novel semantic document classification approach aiming to resolve two types of semantic problems: (1) polysemy problem, by using a novel semantic similarity computing strategy (SSC) and (2) synonym problem, by proposing a novel strong correlation analysis method (SCM). Experiments show that our strategies can help to improve the performance of the baseline methods.

In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.

Web Services are modular, self-describing, self-contained and loosely coupled applications that can be
published, located, and invoked across the web. With the increasing number of web services available on
the web, the need for web services composition is becoming more and more important. Nowadays, for
answering complex needs of users, the construction of new web services based on existing ones is required.
This problem is known as web services composition. However, it is one of big challenge problems of recent
years in a distributed and dynamic environment. The various approaches in field of web service
compositions proposed by the researchers. In this paper we present a review of existing approaches for
web service composition and compare them among each other with respect to some key requirements. We
hope this paper helps researchers to focus on their efforts and to deliver lasting solutions in this field.

... 3. Implementation. Back to Top. Two databases are used in the implementation of the proposed method of semantic similarity; they are WordNet [ 15 ] and the Brown Corpus [ 7 ]. Both databases are publicly available and widely used in previously published works. ...

Statistički pristupi obradi prirodnih jezika tipično zahtevaju značajne količine anotiranih podataka, a često i različite pomoćne jezičke alate, što ograničava njihovu primenu u resursno ograničenim situacijama. U ovoj disertaciji predstavljena je metodologija razvoja statističkih rešenja u semantičkoj obradi prirodnih jezika sa ograničenim resursima. Ovakvi jezici se odlikuju ne samo limitiranim brojem postojećih jezičkih resursa, već i ograničenim mogućnostima za razvoj novih skupova podataka i namenskih alata i algoritama. Predložena metodologija je usredsređena na kratke tekstove zbog njihove rasprostranjenosti u digitalnoj komunikaciji i zbog veće složenosti njihove semantičke obrade.

Metodologija obuhvata sve faze izrade statističkih rešenja, od prikupljanja tekstualnog sadržaja, preko anotacije podataka, do formulisanja, obučavanja i evaluacije modela mašinskog učenja. Njena upotreba je detaljno ilustrovana na dva semantička problema – analizi sentimenta i određivanju semantičke sličnosti. Kao primer jezika sa ograničenim resursima korišćen je srpski jezik, ali se predložena metodologija može primeniti i na druge jezike iz ove kategorije.

Pored opšte metodologije, u doprinose ove disertacije spada razvoj novog, fleksibilnog sistema označavanja sentimenta kratkih tekstova, nove metrike za utvrđivanje ekonomičnosti anotacije, kao i nekoliko novih modela za određivanje semantičke sličnosti kratkih tekstova. Rezultati disertacije uključuju i kreiranje prvih javno dostupnih anotiranih skupova podataka za probleme analize sentimenta i određivanja semantičke sličnosti kratkih tekstova na srpskom jeziku, razvoj i evaluaciju većeg broja modela na ovim problemima, i prvu komparativnu evaluaciju većeg broja alata za morfološku normalizaciju na kratkim tekstovima na srpskom jeziku.

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Statistical approaches to natural language processing typically require considerable amounts of labeled data, and often various auxiliary language tools as well, limiting their applicability in resource-limited settings. This thesis presents a methodology for developing statistical solutions in the semantic processing of natural languages with limited resources. In these languages, not only are existing language resources limited, but so are the capabilities for developing new datasets and dedicated tools and algorithms. The proposed methodology focuses on short texts due to their prevalence in digital communication, as well as the greater complexity regarding their semantic processing.

The methodology encompasses all phases in the creation of statistical solutions, from the collection of textual content, to data annotation, to the formulation, training, and evaluation of machine learning models. Its use is illustrated in detail on two semantic tasks – sentiment analysis and semantic textual similarity. The Serbian language is utilized as an example of a language with limited resources, but the proposed methodology can also be applied to other languages in this category.

In addition to the general methodology, the contributions of this thesis consist of the development of a new, flexible short-text sentiment annotation system, a new annotation cost-effectiveness metric, as well as several new semantic textual similarity models. The thesis results also include the creation of the first publicly available annotated datasets of short texts in Serbian for the tasks of sentiment analysis and semantic textual similarity, the development and evaluation of numerous models on these tasks, and the first comparative evaluation of multiple morphological normalization tools on short texts in Serbian.

This study explores Latent Semantic Analysis as a model of coherence relations in spoken discourse. Two studies were conducted on language data from six adult learners of English observed longitudinally for one year. The studies investigated whether LSA values increase as a function of time spent learning English and if there were links between negotiations for meaning and LSA values. Results show that LSA values increase significantly and that negotiations for meaning decrease significantly over time. Negotiations for meaning are also negatively correlated to LSA values. These findings provide evidence of co-referential semantic development in L2 learners. The findings also demonstrate that a lack of semantic similarity in natural spoken discourse is sufficient to trigger negotiations for meaning.

The virtual world overflowing with the digital items which make the searching, choosing and shopping hard tasks for users. The recommender system is a smart filtering tool for generate a list of potential favorite items for the user to reduce the time needed by user to
choose among a huge number of choices in websites and facilitate the process.
In that context, this thesis presents a novel technique that combines the ideas of item-based semantic similarity, n-criteria and multi-filtering criteria with the genetic-based recommender system. The genetic algorithm is utilized in order to predict the best list of items to the active user. Consequently, each individual in the population represents a candidate recommendation list. Each list subjects to three tests to measure the quality of it.
The proposed system alleviates the effect of the sparsity and cold start problems and makes the recommender system capable of generating recommendation without the need of using a similarity metric or requires any additional information provided by the hybrid system. Furthermore and due to the fact that there are many environments facing the information overload problem, the author presents a new classification of the recommender system based on the environment that is applied in.
The proposed system is evaluated against the state-of-the-art genetic-based recommender system and the traditional techniques that used in collaborative filtering recommender system. The results obtained show that the proposed method outperforms these algorithms in prediction accuracy by 24.3%, recommendation quality by 33.5% and performance (CPU time) by 45.4%. Moreover, the results showed that 69.5% of the recommended items are truly favorite items to the active user. The remainders 30.5% of the recommended items are potential favorite items.

Language samples from matched groups of 80 autistic, mentally handicapped, and normally developing children were coded for the presence of neologisms and~or idiosyncratic language use. Cognitive, socia~ or linguistic factors that might account for these errors were identified and assessed. More autistic subjects used neologisms and idiosyncratic language than age-and language skill-matched control groups. No single factor or combination of factors was responsible for this difference. Across diagnostic groups, similar patterns of error were noted, except that the autistic subjects were more likely to use words inappropriately that had no phonological or semantic similarity to the intended English word. For the autistic groups, the frequency of idiosyncratic language increased with language complexity. In contrast, such errors decreased with language skill in the mentally handicapped group. Theoretical implications for determining the nature and source of the language disorder associated with autism are discussed

The theory of inventive problem solving (TRIZ) Knowledge source Physical-chemical-geometrical effects Semantic similarity Ontology Ontology reasoning a b s t r a c t With the development of the Theory of Inventive Problem Solving (TRIZ), different models and knowledge sources were established in order to solve different types of inventive problems. These knowledge sources with different levels of abstraction are all built independently of the specific application field, and require extensive knowledge about different engineering domains. In order to facilitate the use of these TRIZ models and knowledge sources, an intelligent knowledge management system -IngeniousTRIZ was developed in this research. On the one hand, according to the TRIZ knowledge sources ontologies, this system offers to the users the relevant knowledge sources of the model they are building. On the other hand, the system has the ability to fill ''automatically'' the models of the other knowledge sources. Firstly, the TRIZ user chooses a TRIZ knowledge source to work for an abstract solution. Then, the items of other knowledge sources, which are similar with the selected items of the first knowledge source, are obtained based on semantic similarity calculated in advance. With the help of these similar items and the heuristic physical effects, other concept solutions are generated through ontology inference. In order to show this whole process, the resolution of the case of the ''diving fin'' in IngeniousTRIZ system is elaborated in detail.

—Several attempts have already been made to automate the generation of assessment questions. These attempts were mainly technical and lacked a theoretical backing. We explore psychological and educational theories to support the development of principled methods to generate questions and control their properties. We present a similarity-based theory to control the difficulty of multiple-choice questions and show its practicality and consistency with the psychological and educational theories.

Multiple choice questions (MCQs) are considered highly useful (being easy to take or mark) but quite difficult to create and large numbers are needed to form valid exams and associated practice materials. The idea of re-using an existing ontology to generate MCQs almost suggests itself and has been explored in various projects. In this project, we are applying suitable educational theory regarding assessments and related methods for their evaluation to ontology-based MCQ generation. In particular, we investigate whether we can measure the similarity of the concepts in an ontology with sufficient reliability so that this measure can be used to control the difficulty of the MCQs generated. In this report, we provide an overview of the background to this research, and describe the main steps taken and insights gained.

"Sommaire:
1. LES PÈRES DE L'ÉGLISE
2. RÉPERTOIRE FIGURÉ DU DÉCOR
3. EXPLICATION DES IMAGES AUX CATÉCHUMÈNES
4. LES SYMBOLIQUES DU PRINTEMPS
5. A L'IMAGE DE LA PERFECTION DIVINE
6. MÉTHODE D'INTERPRÉTATION PAR ANALOGIE
7. CYRILLE DE JÉRUSALEM
8. LES ANALOGIES DE L'ÉVÈQUE CYRILLE
9. ÉGLISE DES SAINTS-APÔTRES À MADABA
10. PREMIÈRE ÉGLISE DÉCOUVERTE À PETRA
11. BASILIQUE DE DOUMETIOS À NIKOPOLIS
12. BASILIQUE DE CHRISTOPOLITISSA À PAPHOS
13. ÉGLISE DE QABR HIRAM
14. ÉGLISE DE PHOTIOS À HOUARTÉ
15. ÉGLISE DE MICHAËLION À HOUARTÉ
16. ÉGLISE DE MIRATA
cf. M.T. Olszewski, L'image et sa fonction dans la mosaïque byzantine des premières basiliques en Orient: l'iconographie chrétienne expliquée par Cyrille de Jérusalem (314-387) / The image and its function in the byzantine mosaics ... : the christian iconography explained by Cyril of Jerusalem (314-387), Cahiers Archéologiques 43, 1995, pp. 9-34. "

Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledgebased similarities. Furthermore, samples of combination between these similarities are presented.

Most automatic scoring systems use pattern based that requires a lot of hard and tedious work. These systems work in a supervised manner where predefined patterns and scoring rules are generated. This paper presents a different unsupervised approach which deals with students' answers holistically using text to text similarity. Different String-based and Corpus-based similarity measures were tested separately and then combined to achieve a maximum correlation value of 0.504. The achieved correlation is the best value achieved for unsupervised approach Bag of Words (BOW) when compared to previous work. 120 | P a g e www.ijacsa.thesai.org

Animals have been human's companions from ancient times until now. Humans have observed them, understood their characteristics and even domesticated some species to serve them. People have considered animals as friend, servant or as a source of comfort and encouragement. The relationship with animals and the observation of them provide us with images and ideas as we speak and write. However, in different cultures, the same animal can symbolically express the same concept, or a totally different one. In this research, we will study and analyse a number of animal words in English-Vietnamese proverbs and compare the similarities and differences in their usage, thereby to form an interpretation and translation into English and Vietnamese proverbs accordingly.

Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database and from corpus statistics. The use of a lexical database enables our method to model human common sense knowledge and the incorporation of corpus statistics allows our method to be adaptable to different domains. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition.

... the second line, ie 'as this someone wanted', indicates the controlled nature of the effect on thepatient. ... has to be performed with a tool other than a part of the agent's body and that ... This formulation accounts for the fact that (in English) one does not normally speak about cutting ...

This paper suggests that reference to phenomenal qualities is best understood as involving iconicity, that is, a passage from sign-vehicle to object that exploits a similarity between the two. This contrasts with a version of the ‘phenomenal concept strategy’ that takes indexicality to be central. However, since it is doubtful that phenomenal qualities are capable of causally interacting with anything, indexical reference seems inappropriate. While a theorist like David Papineau is independently coming to something akin to iconicity, I think some of the awkwardness that plagues his account would be remedied by transitioning to a more inclusive philosophy of signs.

A new approach to numerically measure the semantic distances between lexical units (words and collocations) based on the geometric analogies and analytical calculations, is put forward. Having considered the cases of equal and different weights of semes, we obtained exact algebraic formulas describing different levels of the meanings proximity, ranging from absolute synonymy to full antonymy. It was emphasized that absolute synonymy arises when the compared units contain equal numbers of semes that fully coincide and have equal weights in the corresponding pairs. Calculation of the semes weights helps to locate the unit more precisely on the semantic sphere. It was shown that the level of synonymy and antonymy decreases if different semes are accentuated, while the semantic distance between the units without identical semes cannot be influenced by seme boosting. It was observed that depending on the context, a word may wander over this sphere, thus modifying its lexical semantic relations with other units. As the proposed approach contributes to formalization of the units comparison procedure, it is advisable for relevant automatic tools, particularly for WordNets and FrameNet. The obtained results may be useful for various linguistic and associated studies including automatic text analysis and processing, computer lexicography, information search and retrieval, machine translation and other NLP applications that are related to the artificial intelligence problem.

14th International Conference on Web services & Semantic Technology (WeST
2022) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of web & semantic technology. The growth of the
World-Wide Web today is simply phenomenal. It continues to grow rapidly and new
technologies, applications are being developed to support end users modern life. Semantic
Technologies are designed to extend the capabilities of information on the Web and
enterprise databases to be networked in meaningful ways.

WordNet proved that it is possible to construct a large-scale electronic lexical database on the principles of lexical semantics. It has been accepted and used extensively by computational linguists ever since it was released. Inspired by WordNet's success, we propose as an alternative a similar resource, based on the 1987 Penguin edition of Roget's Thesaurus of English Words and Phrases.

In recent years, a web phenomenon known as Volunteered Geographic Information (VGI) has produced large crowdsourced geographic data sets. OpenStreetMap (OSM), the leading VGI project, aims at building an open-content world map through user contributions. OSM semantics consists of a set of properties (called ‘tags’) describing geographic classes, whose usage is defined by project contributors on a dedicated Wiki website. Because of its simple and open semantic structure, the OSM approach often results in noisy and ambiguous data, limiting its usability for analysis in information retrieval, recommender systems and data mining. Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap. The contribution of this paper is twofold. It consists of (1) the development of the OSM Semantic Network by means of a web crawler tailored to the OSM Wiki website; this semantic network can be used to compute semantic similarity through co-citation measures, providing a novel semantic tool for OSM and GIS communities; (2) a study of the cognitive plausibility (i.e. the ability to replicate human judgement) of co-citation algorithms when applied to the computation of semantic similarity of geographic concepts. Empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network.

Abstract. Enabling the Semantic Web requires solving the semantic heterogeneity problem, for which ontology matching methods have been proposed. These methods rely on similarity measures that are mainly focused on terminological, structural and extensional properties of the ontologies. Semantics rarely play a direct role on the ontology matching process, albeit some algorithms have been proposed. On the other hand, many ontology engineers choose representation languages that have an underlying formal logic, providing well-defined ...

Internet becomes the most popular surfing environment which increases the service oriented data size. As the data size grows, finding and retrieving the most similar data from the large volume of data would become more difficult task. This problem is focused in the various research methods, which attempts to cluster the large volume of data. In the existing research method Clustering-based Collaborative Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar kind of data together, so that retrieval time cost can be reduced considerably. However, existing research methods cannot find the similar reviews accurately which needs to be focused more for efficient and accurate recommendation system. This is ensured in the proposed research method by introducing the novel research technique namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR). In this research method, initially k means algorithm is used to cluster the similar movie reviewer together, so that recommendation process can be done in the easier way. In order to handle the large volume of data this research work adapts the map reduce framework which will divide the entire data into subsets which will assigned on separate nodes with individual key values. After clustering, the clustered outcome is merged together using inverted index procedure in which similarity between movies would be calculated. Here collaborative filtering is applied to remove the movies that are not relevant to input. Finally recommendations of movies are made in the accurate way by using the logistic regression method. The overall evaluation of the proposed research method is done in Hadoop from which it can be proved that the proposed research technique can lead to provide better outcome than the existing research techniques.

This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.

This paper presents a new corpus-based method for calculating the semantic similarity of two target words. Our method, called Second Order Co-occurrence PMI (SOC-PMI), uses Pointwise Mutual Information to sort lists of important neighbor words of the two target words. Then we consider the words which are common in both lists and aggregate their PMI values (from the opposite list) to calculate the relative semantic similarity. Our method was empirically evaluated using Miller and Charler's (1991) 30 noun pair subset, 65 noun pairs, 80 synonym test questions from the Test of English as a Foreign Language (TOEFL), and 50 synonym test questions from a collection of English as a Second Language (ESL) tests. Evaluation results show that our method outperforms several competing corpus-based methods.

A search results ranking method that uses semantic features and a cluster coherence measure is introduced in this paper. The quality of the returned search results is improved by grouping semantically related texts into clusters displayed in descending cluster size order. First the term-document matrix is constructed where the documents correspond to individual texts. Then, nonnegative matrix factorization (NMF) is used to group the texts into semantically related clusters. Only those clusters whose coherence is greater than a threshold value are displayed. In this way trending conceptually similar texts that re-occur in the input of multiple users are identified. The advantage of this approach compared to other methods [6] consists in the fact that the clusters in the approach introduced in this paper are computed by semantic similarity and not only by texts counters.

Birden fazla kavram arasında kurulan benzerlik, yakınlık, yaklaşıklık, denklik ve özdeşlik ilişkileri; kavramların doğrudan ya da dolaylı olarak karşılaştırılmasını, birbirlerine göre konumlanmasını, bunlar hakkında kesin ya da tahminî değerlendirmeler yapılmasını, türlü özelliklerin daha somut biçimde iletilmesini sağlar. Nitelik ya da nicelik açısından kavramlar arasındaki benzerliklere, görece ve aşamalı yakınlıklara, eş değerliklere, özdeşliklere dayanan bu ifade biçimlerine; ilişkilendirmenin ve somutlaştırmanın güçlü olduğu etkili bir dil kullanımı için başvurulur. Dil kullanıcıları da söz konusu anlam ilişkilerini, dilin kimi özelliklerinden, sunduğu anlatım olanaklarından yararlanarak gerçekleştirir. Bu kapsamda dilin sözlüksel, biçim bilimsel, anlam bilimsel, söz dizimsel özellikleri başta olmak üzere dilsel ve kültürel pek çok bileşen etkin rol oynar. Türkiye Türkçesinin ölçünlü dilinde de bu tür anlam ilişkilerinin kurulabilmesi amacıyla eklere ve ek birleşimlerine, sözcüklere, sözcük gruplarına, eklerle sözcüklerden ya da birden fazla sözcükten oluşan anlatım kalıplarına, deyimlere ve aktarmalara başvurulmakta; çok sayıda sözlüksel, biçim bilimsel, söz dizimsel ve anlam bilimsel işaretleyici kullanılmaktadır. Bu çalışmada, büyük çoğunluğu Cumhuriyet sonrası Türk edebiyatının roman, hikâye, şiir, eleştiri, deneme, mektup, biyografi gibi türlerinde verilmiş eserlerinden hareketle benzerlik, yakınlık, yaklaşıklık, denklik ve özdeşlik ifadelerinin hangi işaretleyicilerle sağlandığı belirlenmiş ve örneklenmiş; bu ifadelerin verilme yolları incelenmiştir. Çalışmada yararlanılan örnekler, belirtilen eserlerin taranmasıyla elde edilmiştir.

Similarity, proximity, approximation, equivalence and identity relationships constituted between multiple concepts provide direct or indirect comparison of concepts, positioning relative to each other, making precise or predictive evaluations about them and transmitting various features in a more concrete way. These forms of expression based on similarities between concepts in terms of quality or quantity, relative and gradual proximity, equivalences, identities are used for an effective language that is strong in association and concretization. So language users actualise the meaning relations by taking advantage of the some linguistic features and the possibilities of expression. In this context, many linguistic and cultural components play an active role, especially lexical, morphologic, semantic, syntactic features of language. Also in Turkey Turkish standart language, affix and affix combination, words, word groups, expression patterns consisting of affixes and word or more than one word, idioms and metaphrases are utilised and numerous lexical, morpholgic, syntactic and semantic markers are used in order to establish these kind of semantic relations. In this study, with reference to majority of the works of the post-republic Turkish literature in the genres such as novel, story, poem, criticism, essay, letter and biography; the similarity, proximity, approximation, equivalence and identity expressions are provided by which markers were determined and exemplified and in which way these expressions are given have been examined. The samples used in the study were obtained by scanning the mentioned works.

Measuring the semantic similarity between words, sentences and concepts is an important task in information retrieval, document clustering, web mining and word sense disambiguation. Semantic similarity is basically a measure used to compute the extent of similarity between two concepts based on the likeliness of their meaning. This survey discusses the existing similarity measures by partitioning them into two approaches: Corpus-based and Knowledge-based. The features, performance, advantages and disadvantages of various semantic similarity measures are discussed. The aim of this paper is to provide an efficient evaluation of all these measures and help the researchers to select the best measure according to their requirement.

We propose a statistical measure for the degree of acceptability of light verb constructions, such as take a walk, based on their linguistic properties. Our measure shows good correlations with human ratings on unseen test data. Moreover, we find that our measure correlates more strongly when the potential complements of the construction (such as walk, stroll, or run) are separated into semantically similar classes. Our analysis demonstrates the systematic nature of the semi-productivity of these constructions.