Wordnet

Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.

Noun phrases in queries are identified and classified into four types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in the phrase are within a window of a certain size. The window sizes for different types of phrases are different and are determined using a decision tree. Phrases are more important than individual terms. Consequently, documents in response to a query are ranked with matching phrases given a higher priority. We utilize WordNet to disambiguate word senses of query terms. Whenever the sense of a query term is determined, its synonyms, hyponyms, words from its definition and its compound words are considered for possible additions to the query. Experimental results show that our approach yields between 23% and 31% improvements over the best-known results on the TREC 9, 10 and 12 collections for short (title only) queries, without using Web data.

This paper describes our work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing. The construction of each of these lexical resources has required many years of laborious human effort, and they all have their strengths and shortcomings. By linking them together, we build an improved resource in which (1) the coverage of FrameNet is extended, (2) the VerbNet lexicon is augmented with frame semantics, and (3) selectional restrictions are implemented using WordNet semantic classes. The synergistic exploitation of various lexical resources is crucial for many complex language processing applications, and we prove it once again effective in building a robust semantic parser.

The information content (IC) of a concept provides an estimation of its degree of generality/concreteness, a dimension which enables a better understanding of concept's semantics. As a result, IC has been successfully applied to the automatic assessment of the semantic similarity between concepts. In the past, IC has been estimated as the probability of appearance of concepts in corpora. However, the applicability and scalability of this method are hampered due to corpora dependency and data sparseness. More recently, some authors proposed IC-based measures using taxonomical features extracted from an ontology for a particular concept, obtaining promising results. In this paper, we analyse these ontology-based approaches for IC computation and propose several improvements aimed to better capture the semantic evidence modelled in the ontology for the particular concept. Our approach has been evaluated and compared with related works (both corpora and ontology-based ones) when applied to the task of semantic similarity estimation. Results obtained for a widely used benchmark show that our method enables similarity estimations which are better correlated with human judgements than related works.

This paper presents a new open text word sense disambiguation method that combines the use of logical inferences with PageRank-style algorithms applied on graphs extracted from natural language documents. We evaluate the accuracy of the proposed algorithm on several sense-annotated texts, and show that it consistently outperforms the accuracy of other previously proposed knowledge-based word sense disambiguation methods. We also explore and evaluate methods that combine several open-text word sense disambiguation algorithms.

This paper presents a new model to measure semantic similarity in the taxonomy of WordNet, using edgecounting techniques. We weigh up our model against a benchmark set by human similarity judgment, and achieve a much improved result compared with other methods: the correlation with average human judgment on a standard 28 word pair dataset is 0.921, which is better than anything reported in the literature and also significantly better than average individual human judgments. As this set has been effectively used for algorithm selection and tuning, we also cross-validate an independent 37 word pair test set (0.876) and present results for the full 65 word pair superset (0.897).

We describe in this paper a boolean Information l~.etrieval system that adds word semantics to the classic word based indexing. Two of the main tasks of our system, namely the indexing and retrieval components, are using a combined wordbased and sense-based approach.

WORDNET, a ubiquitous tool for natural language processing, suffers from sparsity of connections between its component concepts (synsets). Through the use of human annotators, a subset of the connections between 1000 hand-chosen synsets was assigned a value of "evocation" representing how much the first concept brings to mind the second. These data, along with existing similarity measures, constitute the basis of a method for predicting evocation between previously unrated pairs.

Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification.

Latest research efforts on the semi-automatic coordination of ontologies "touch" on the mapping /merging of ontologies using the whole breadth of available knowledge. Addressing this issue, this paper presents the HCONE-merge approach, which is further extended towards automating the merging process. HCONE-merge makes use of the intended informal meaning of concepts by mapping them to WordNet senses using the Latent Semantic Indexing (LSI) method. Based on these mappings and using the reasoning services of Description Logics, HCONE-merge automatically aligns and then merges ontologies. Since the mapping of concepts to their intended meaning is an essential step of the HCONE-merge approach, this paper explores the level of human involvement required for mapping concepts of the source ontologies to their intended meanings. We propose a series of methods for ontology mapping (towards merging) with varying degrees of human involvement and evaluate them experimentally. We conclude that, although an effective fully automated process is not attainable, we can reach a point where ontology merging can be carried out efficiently with minimum human involvement. the intended meanings of terms by means of heuristic rules (e.g. [5], [7], and [12]). The HCONE-merge approach to ontology merging [8] [9] exploits all the above-mentioned types of knowledge. This approach gives much emphasis on "uncovering" the intended informal meaning of concepts specified in an ontology by mapping them to WordNet senses. WordNet senses realize the informal, human-oriented intended meaning of the corresponding concepts. To compute these mappings, HCONE-merge uses the Latent Semantic Indexing (LSI) method. Exploiting the mappings proposed by LSI, the merging method we introduce translates concept definitions of the source ontologies to a common vocabulary and finally, merges the translated definitions by means of specific merging rules and description logics' reasoning services.

A class of semantic theories defines concepts in terms of statistical distributions of lexical items, basing meaning on vectors of word co-occurrence frequencies. A different approach emphasizes abstract hierarchical taxonomic relationships among concepts. However, the functional relevance of these different accounts and how they capture information-encoding of lexical meaning in the brain still remains elusive. We investigated to what extent distributional and taxonomic models explained word-elicited neural responses using cross-validated representational similarity analysis (RSA) of functional magnetic resonance imaging (fMRI) and model comparisons. Our findings show that the brain encodes both types of semantic information, but in distinct cortical regions. Posterior middle temporal regions reflected lexical-semantic similarity based on hierarchical taxonomies, in coherence with the action-relatedness of specific semantic word categories. In contrast, distributional semantics best predicted the representational patterns in left inferior frontal gyrus (LIFG, BA 47). Both representations coexisted in the angular gyrus supporting semantic binding and integration. These results reveal that neuronal networks with distinct cortical distributions across higher-order association cortex encode different representa-tional properties of word meanings. Taxonomy may shape long-term lexical-semantic representations in memory consistently with the sensorimotor details of semantic categories, whilst distributional knowledge in the LIFG (BA 47) may enable semantic combinatorics in the context of language use. Our approach helps to elucidate the nature of semantic representations essential for understanding human language .

ABSTRACT Several branches of computer vision heavily rely (but we could even say depend) on the availability of large datasets of labelled images. While such labeling is usually done by hand, a powerful help can be obtained from Internet and its related tools. In this paper we address the problem of automatically generating a set of images representing an object class, given the name of the class. We exploit semantic technologies, such as lexical resources and ontologies, in order to improve the search performances by using a standard web search engine. We will also discuss an application to the automatic building of a training set for a classification framework. Preliminary experiments are provided for 10 classes from the public CalTech256 dataset and results show an average increment in classification accuracy of about 10%.

This paper presents a new approach to determine the senses of words in queries by using WordNet. In our approach, noun phrases in a query are determined first. For each word in the query, information associated with it, including its synonyms, hyponyms, hypernyms, definitions of its synonyms and hyponyms, and its domains, can be used for word sense disambiguation. By comparing these pieces of information associated with the words which form a phrase, it may be possible to assign senses to these words. If the above disambiguation fails, then other query words, if exist, are used, by going through exactly the same process. If the sense of a query word cannot be determined in this manner, then a guess of the sense of the word is made, if the guess has at least 50% chance of being correct. If no sense of the word has 50% or higher chance of being used, then we apply a Web search to assist in the word sense disambiguation process. Experimental results show that our approach has 100% applicability and 90% accuracy on the most recent robust track of TREC collection of 250 queries. We combine this disambiguation algorithm to our retrieval system to examine the effect of word sense disambiguation in text retrieval. Experimental results show that the disambiguation algorithm together with other components of our retrieval system yield a result which is 13.7% above that produced by the same system but without the disambiguation, and 9.2% above that produced by using Lesk's algorithm. Our retrieval effectiveness is 7% better than the best reported result in the literature.

Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of generated ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving , student training and skill acquisition. A selected subset of information content (IC Sánchez-Batet) and semantic similarity (Lin/Sánchez-Batet) measures, which are both statistically powerful and computation-ally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence .

Question Answering Systems, unlike search engines, are providing answers to the users' questions in succinct form which requires the prior knowledge of the expectation of the user. Question classification module of a Question Answering System plays a very important role in determining the expectations of the user. In the literature, incorrect question classification has been cited as one of the major factors for the poor performance of the Question Answering Systems and this emphasizes on the importance of question classification module designing. In this article, we have proposed a question classification method that exploits the powerful semantic features of the WordNet and the vast knowledge repository of the Wikipedia to describe informative terms explicitly. We have trained our system over a standard set of 5500 questions (by UIUC) and then tested it over five TREC question collections. We have compared our results with some standard results reported in the literature and observed a significant improvement in the accuracy of question classification. The question classification accuracy suggests the effectiveness of the method which is promising in the field of open-domain question classification. Judging the correctness of the answer is an important issue in the field of question answering. In this article, we are extending question classification as one of the heuristics for answer validation. We are proposing a World Wide Web based solution for answer validation where answers returned by open-domain Question Answering Systems can be validated using online resources such as Wikipedia and Google. We have applied several heuristics for answer validation task and tested them against some popular web based open-domain Question Answering Systems over a collection of 500 questions collected from standard sources such as TREC, the Worldbook, and the Worldfactbook. The proposed method seems to be promising for automatic answer validation task.

The star schema is widely accepted as the de facto data model for data warehouse design. A popular approach for developing a star schema is to develop it from an entity-relationship diagram with some heuristics. Most of the existing approaches analyze the semantics of an ERD to generate a star schema. In this paper, we present the SAMSTAR method, which semi-automatically generates star schemas from an ERD by analyzing its semantics as well as structure. The novel features of SAMSTAR are (1) the use of the notion of Connection Topology Value (CTV) in identifying the candidates of facts and dimensions and (2) the use of Annotated Dimensional Design Patterns (A_DDP) as well as WordNet to extend the list of dimensions. We illustrate our method by applying it to the examples from existing literature. We prove that the outputs of our method are a superset of those of the existing methods. The SAMSTAR method simplifies the work of experienced designers and gives a smooth head-start to novices.

Princeton WordNet (PWN) is one of the most influential resources for semantic descriptions, and is extensively used in natural language processing. Based on PWN, three Chinese wordnets have been developed: Sinica Bilingual Ontological Wordnet (BOW), Southeast University WordNet (SEW), and Taiwan University WordNet (CWN). We used SEW to sense-tag a corpus, but found some issues with coverage and precision. We decided to make a new Chinese wordnet based on SEW to increase the coverage and accuracy. In addition, a small scale Chinese wordnet was constructed from open multilingual wordnet (OMW) using data from Wiktionary (WIKT). We then merged SEW and WIKT. Starting from core synsets, we formulated guidelines for the new Chinese Open Wordnet (COW). We compared the five Chinese wordnets, which shows that COW is currently the best, but it still has room for further improvement, especially with polysemous words. It is clear that building an accurate semantic resource for a language is not an easy task, but through consistent efforts, we will be able to achieve it. COW is released under the same license as the PWN, an open license that freely allows use, adaptation and redistribution.

The whole world is changed rapidly and using the current technologies Internet becomes an essential
need for everyone. Web is used in every field. Most of the people use web for a common purpose like
online shopping, chatting etc. During an online shopping large number of reviews/opinions are given by
the users that reflect whether the product is good or bad. These reviews need to be explored, analyse and
organized for better decision making. Opinion Mining is a natural language processing task that deals
with finding orientation of opinion in a piece of text with respect to a topic. In this paper a document
based opinion mining system is proposed that classify the documents as positive, negative and neutral.
Negation is also handled in the proposed system. Experimental results using reviews of movies show the
effectiveness of the system.

This paper reports on a study to explore how semantic relations can be used to expand a query for objects in an image. The study is part of a project with the overall objective to provide semantic annotation and search facilities for a virtual collection of art resources. In this study we used semantic relations from WordNet for 15 image content queries. The results show that, next to the hyponym/hypernym relation, the meronym/holonym (part-of) relation is particularly useful in query expansion. We identified a number of relation patterns that improve recall without jeopardising precision.

Capturing the sentiments and the emotional states enclosed in textual information is a critical task which embraces a wide range of web-oriented activities such as detecting the sentiments associated to the product reviews, developing marketing programs that would be attractive for users, enhancing customer service with respect to its expectation until to identifying new opportunities and financial market prediction, besides managing reputations. Opinions and the emotions that are embedded in them, play a key role in decision-making processes, with different effects depending on the negative or positive valence of the mood. When the choice depends on some important features (i.e., time, money, reliability/efficacy, etc.) and on other opinions (which come from previous experience), could be crucial to make the best decision.

In the field of process mining, the goal is to automatically extract process models from event logs. Recently, many algorithms have been proposed for this task. For comparing these models, different quality measures have been proposed. Most of these measures, however, have several disadvantages; they are model-dependent, assume that the model that generated the log is known, or need negative examples of event sequences. In this paper we propose a new measure, based on the minimal description length principle, to evaluate the quality of process models that does not have these disadvantages. To illustrate the properties of the new measure we conduct experiments and discuss the trade-off between model complexity and compression.

Two novel word clustering techniques are proposed which employ long distance bigram language models. The first technique is built on a hierarchical clustering algorithm and minimizes the sum of Mahalanobis distances of all words after a cluster merger from the centroid of the class created by merging. The second technique resorts to probabilistic latent semantic analysis (PLSA). Next, interpolated long distance bigrams are considered in the context of the aforementioned clustering techniques. Experiments conducted on the English Gigaword corpus (second edition) demonstrate that: (1) the long distance bigrams, when employed in the two clustering techniques under study, yield word clusters of better quality than the baseline bigrams; (2) the interpolated long distance bigrams outperform the long distance bigrams in the same respect; (3) the long distance bigrams perform better than the bigrams, which incorporate trigger-pairs selected at various distances; and (4) the best word clustering is achieved by the PLSA that employs interpolated long distance bigrams. Both proposed techniques outperform spectral clustering based on k-means. To assess objectively the quality of the created clusters, relative cluster validity indices are estimated as well as the average cluster sense precision, the average cluster sense recall, and the F-measure are computed by exploiting ground truth extracted from the WordNet.

In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the representation of text documents. The basic hypothesis examined is that considering semantic relatedness between two text documents may improve the performance of the text document clustering task. For our experimental evaluation we analyze the performance of several semantic relatedness measures when embedded in the proposed (S-VSM) and present results with respect to different experimental conditions, such as: (i) the datasets used, (ii) the underlying knowledge sources of the utilized measures, and (iii) the clustering algorithms employed. To the best of our knowledge, the current study is the first to systematically compare, analyze and evaluate the impact of semantic smoothing in text clustering based on 'wisdom of linguists', e.g., WordNets, 'wisdom of crowds', e.g., Wikipedia, and 'wisdom of corpora', e.g., large text corpora represented with the traditional Bag of Words (BoW) model. Three semantic relatedness measures for text are considered; two knowledge-based (Omiotis [1] that uses WordNet, and WLM [2] that uses Wikipedia), and one corpus-based (PMI [3] trained on a semantically tagged SemCor version). For the comparison of different experimental conditions we use the BCubed F-Measure evaluation metric which satisfies all formal constraints of good quality cluster. The experimental results show that the clustering performance based on the S-VSM is better compared to the traditional VSM model and compares favorably against the standard GVSM kernel which uses word co-occurrences to compute the latent similarities between document terms. 1 Though there are slight conceptual differences between the terms 'semantic similarity' and 'semantic relatedness', for the purposes of this work this differentiation is not important. Therefore, the two terms might be used interchangeably for the remaining of the paper.

This paper will focus on recent and near-term future developments at FrameNet (FN) and the interoperability issues they raise. We begin by discussing the current state of the Berkeley FN database including major changes in the data format for the latest data release. We then briefly review two recent local projects, ''Rapid Vanguarding'', which has created a new interface for the frame and lexical unit definition process based on the Word Sketch Engine of , and ''Beyond the Core'', which has developed tools for annotating constructions, and created a sample ''construction'' of especially ''interesting'' constructions which are neither simply lexical nor easy for the standard parsers to parse. We also cover two current collaborations, FN's part in the development of the manually annotated subcorpus of the American National Corpus, and a pilot study on aligning WordNet and FrameNet, to exploit the complementary strengths of these quite different resources. We discuss FN-related research on Spanish, Japanese, German (SALSA), Chinese and other languages, and the language-independence of frames, along with interesting FN-related work by others, and a sketch of a large group of imageschematic frames which are now being added to FN. We close with some ideas about how FrameNet can be opened up, to allow broader participation in the development process without losing precision and coherence, including a smallscale study on acquiring data for FN using Amazon's Mechanical Turk crowdsourcing system.

This paper examines and proposes the automatic generation of concept hierarchies using WordNet. Existing researches have mostly explored the utilization of concept hierarchies, but have not addressed the prohibitive cost occurred in building large hierarchies manually. Several studies have examined the automatic generation of concept hierarchies for the numerical type data from a database. However, very little is known about the automatic generation of concept hierarchies for the nominal type data from a database, which is the subject of this paper. We propose the WordNet library method that first eliminates the ambiguity of the senses of nominal data values, constructs the concept hierarchy by overlapping the hypernym of the remaining senses, and lastly adjusts the resultant concept hierarchy to the preference of users. The proposed method is tested with a faculty employment database of a university. The automatic generation of hierarchies turns out to save efforts of experts or designers who build the concept hierarchies, and makes the hierarchy more objectively built than it is manually done.

In this paper, we propose a probabilistic graphical model to represent weakly annotated images. We consider an image as weakly annotated if the number of keywords defined for it is less than the maximum number defined in the ground truth. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in visual-textual classification and automatic annotation of images. The visualtextual classification is performed by using both visual and textual information. The experimental results, obtained from a database of more than 30000 images, show an improvement by 50.5% in terms of recognition rate against only visual information classification. Taking into account semantic relations between keywords improves the recognition rate by 10.5%. Moreover, the proposed model can be used to extend existing annotations to weakly annotated images, by computing distributions of missing keywords. Semantic relations improve the mean rate of good annotations by 6.9%. Finally, the proposed method is competitive with a state-of-art model.

We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: on the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and outdegree distributions. A particularly interesting result concerns the existence of a typical scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network.

This paper presents an algorithm for aligning FrameNet lexical units to WordNet synsets. Both, FrameNet and WordNet, are well-known as well as widely-used resources by the entire research community. They help systems in the comprehension of the semantics of texts, and therefore, finding strategies to link FrameNet and WordNet involves challenges related to a better understanding of the human language. Such deep analysis is exploited by researchers to improve the performance of their applications. The alignment is achieved by exploiting the particular characteristics of each lexical-semantic resource, with special emphasis on the explicit, formal semantic relations in each. Semantic neighborhoods are computed for each alignment of lemmas, and the algorithm calculates correlation scores by comparing such neighborhoods. The results suggest that the proposed algorithm is appropriate for aligning the FrameNet and WordNet hierarchies. Furthermore, the algorithm can aid research on increasing the coverage of FrameNet, building FrameNets in other languages, and creating a system for querying a joint FrameNet-WordNet hierarchy.

Text processing has been playing a great role in information retrieval to solve the problem of ambiguity in natural language processing, e.g., internet search, data mining, and social media. In semantic similarity, it will be used to analyze the relationships between Word-Pairs on social media. Organizing a huge number of unstructured text documents into a small number of concepts of word sense disambiguation is essential so that the lexical source could incorporate the features for capturing more semantic evidence. Text mining involves the pre-processing of documents collections, text categorization and classification, and extracting information and terms from golden standard data sets. This work proposed the lexical sourced from the semantic representation. The paper contained an evaluation of the advanced measures, which include shortest path, depth, and information content measures. In this paper, we used the same set of measures as previous studies, but different methods such as taxonomy on social media by semantic similarities, such as Synonymy (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/ Synonym.txt), Non-taxonomy, Hypernym, and Glosses. This paper has focused to address the synonymy and ambiguity by incorporating the knowledge in the lexical resources. Thus, each word in a document is linked to its corresponding concept in the lexical resources. To build the semantic representation, these approaches can be classified into two main approaches: knowledge-based and statistical approaches. The knowledge-based approaches depend on structured information that is normally available in forms of dictionaries, thesaurus, lexicons, WordNet 3.1, and ontologies.

The linked open data (LOD) paradigm has emerged as a promising approach to structuring and sharing geospatial information. One of the major obstacles to this vision lies in the difficulties found in the automatic integration between heterogeneous vocabularies and ontologies that provides the semantic backbone of the growing constellation of open geo-knowledge bases. In this article, we show how to utilize WordNet as a semantic hub to increase the integration of LOD. With this purpose in mind, we devise Voc2WordNet, an unsupervised mapping technique between a given vocabulary and WordNet, combining intensional and extensional aspects of the geographic terms. Voc2WordNet is evaluated against a sample of human-generated alignments with the OpenStreetMap (OSM) Semantic Network, a crowdsourced geospatial resource, and the GeoNames ontology, the vocabulary of a large digital gazetteer. These empirical results indicate that the approach can obtain high precision and recall.

In this paper, we develop formal computational models for three aspects of experiential systems for browsing media-(a) context (b) interactivity through hyper-mediation and (c) context evolution using a memory model. Experiential systems deal with the problem of developing context adaptive mechanisms for knowledge acquisition and insight. Context is modeled as a union of graphs whose nodes represent concepts and where the edges represent the semantic relationships. The system context is the union of the contexts of the user, the environment and the media being accessed. We also develop a novel concept dissimilarity. We then develop algorithms to determine the optimal hyperlink for each media element by determining the relationship between the user context and the media. As the user navigates through the hyper-linked sources, the memory model captures the interaction of the user with the hyper-linked sources and updates the user context. Finally, this results in new hyper-links for the media. Our pilot user studies show excellent results, validating our framework.

The semantic similarity between two concepts is widely used in natural language processing. In this article, we propose a method using WordNet 3.1 to determine the similarity based on feature combinations. This work focuses on overcoming the ambiguity in social media text via the selection of informative features to improve semantic representation. In addition, this research uses social media as its research domain used in this work, and the study is only limited to the politic dataset. A feature-based method is applied to predict the outcome and improve the performance of the proposed method depending on factors related to the fidelity, continuity, and balance of knowledge sources in WordNet 3.1. Semantic similarity measurements among words are insufficient and unbalanced features. However, this study presents a semantic similarity measure of a feature-based method in 1 WordNet 3.1 to determine the similarity between two concepts/words depending on the selected features used to measure their similarity, which is also known as a "noun" and "is-a" relations-based method. We evaluate our proposed method using the data set in Agirre et al. (2009) (2 AG203) and compare our results of our new method as which three of methods taxonomy relation, non-taxonomy and Glosses with those of related studies. The correlation with human judgments is subjective and low based on our results was a better. Experimental results show that our new method significantly outperforms other existing computational methods with the following results: r = 0.73%, p = 0.69%, m = 0.71% and nonzero = 0.95%.

A number of Learning Management Systems (LMSs) exist on the market today. A subset of a LMS is the component in which student assessment is managed. In some forms of assessment, such as open questions, the LMS is incapable of evaluating the students' responses and therefore human intervention is necessary. In order to assess at higher levels of Bloom's (1956) taxonomy, it is necessary to include open-style questions in which the student is given the task as well as the freedom to arrive at a response without the comfort of recall words and/or phrases. Automating the assessment process of open questions is an area of research that has been ongoing since the 1960s. Earlier work focused on statistical or probabilistic approaches based primarily on conceptual understanding. Recent gains in Natural Language Processing have resulted in a shift in the way in which free text can be evaluated. This has allowed for a more linguistic approach which focuses heavily on factual understanding. This study will leverage the research conducted in recent studies in the area of Natural Language Processing, Information Extraction and Information Retrieval in order to provide a fair, timely and accurate assessment of student responses to open questions based on the semantic meaning of those responses.

This paper describes a rule-based semantic parser that relies on a frame dataset (FrameNet), and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.

Metaphor and analogy are perhaps the most challenging aspects of linguistic creativity for a conceptual representation to facilitate, since by their very nature they seek to stretch the boundaries of domain description and dynamically establish new ways of determining inter-domain similarity. By solving the vexing representational problems posed by these phenomena, we can create a more fluid conceptual organization that is more suited to creative processing in general. Toward this end, this paper considers the problem of how a conceptual system structured around a central taxonomy can dynamically create new categories or types to understand creative metaphors and analogies. We demonstrate that the conventional wisdom regarding metaphor and analogy -that such processes are creative because clever word-play is indicative of an underlying mental agility and suppleness of conceptual structure -also withstands theoretical scrutiny when considered from the perspective of current creativity research.

Sound engineers need to access vast collections of sound effects for their film and video productions. Sound effects providers rely on text-retrieval techniques to offer their collections. Currently, annotation of audio content is done manually, which is an arduous task. Automatic annotation methods, normally fine-tuned to reduced domains such as musical instru ments or reduced sound effects taxonomies, are not mature enough for labeling with great detail any possible sound. A general s ound recognition tool would require: first, a taxonomy that represents the world and, second, thousands of classifiers, each sp ecialized in distinguishing little details. We report experimental results on a general sound annotator. To tackle the taxonomy definition problem we use WordNet, a semantic network that organizes real world knowledge. In order to overcome the need of a huge number of classifiers to distinguish many different sound classes, we use a nearest-neighbor classifier with a database of isolated sounds unambiguously linked to WordNet concepts. A 30% concept prediction is achieved on a database of over 50.000 s ounds and over 1600 concepts.

This paper presents a set of methodolo-gies and algorithms to create WordNets following the expand model. We explore dictionary and BabelNet based strategies, as well as methodologies based on the use of parallel corpora. Evaluation results for six languages are presented: Catalan, Spanish, French, German, Italian and Por-tuguese. Along with the methodologies and evaluation we present an implementation of all the algorithms grouped in a set of programs or toolkit. These programs have been successfully used in the Know2 Project for the creation of Catalan and Spanish WordNet 3.0. The toolkit is published under the GNU-GPL license and can be freely downloaded from http: //lpg.uoc.edu/wn-toolkit.

FrameNet is a large-scale lexical resource encoding information about semantic frames (situations) and semantic roles. The aim of the paper is to enrich FrameNet by mapping the lexical fillers of semantic roles to WordNet using a Wikipediabased detour. The applied methodology relies on a word sense disambiguation step, in which a Wikipedia page is assigned to a role filler, and then BabelNet and YAGO are used to acquire WordNet synsets for a filler. We show how to represent the acquired resource in OWL, linking it to the existing RDF/OWL representations of FrameNet and WordNet. Part of the resource is evaluated by matching it with the WordNet synsets manually assigned by FrameNet lexicographers to a subset of semantic roles.

We describe building a large-scale image ontology using the WordNet lexical resources. This ontology is based on English words identifying portrayable objects. We reviewed the upper structure and interconnections of WordNet and selected only the branches leading to portrayable objects. This article explains our pruning approach to WordNet. The words, which represent portrayable objects, are then used as queries in our VIKA (VIsual KAtaloguer) system which acquires images through a web image search engine, performs content-based image indexing and clustering. Coherent images form clusters and others are rejected. So images inside clusters mostly represent the object determined by the query, and in this way image collections representing objects are created. An ontology of portrayable objects with representative images in its nodes will be a useful tool for solving the object recognition task

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.