Showing papers on "Semantic similarity published in 2012"

Hamming Distance Metric Learning

Abstract: Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity. Binary codes are well suited to large-scale applications as they are storage efficient and permit exact sub-linear kNN search. The framework is applicable to broad families of mappings, and uses a flexible form of triplet ranking loss. We overcome discontinuous optimization of the discrete mappings by minimizing a piecewise-smooth upper bound on empirical loss, inspired by latent structural SVMs. We develop a new loss-augmented inference algorithm that is quadratic in the code length. We show strong retrieval performance on CIFAR-10 and MNIST, with promising classification results using no more than kNN on the binary codes.

Distributional Semantics in Technicolor

Abstract: Our research aims at building computational models of word meaning that are perceptually grounded. Using computer vision techniques, we build visual and multimodal distributional models and compare them to standard textual models. Our results show that, while visual models with state-of-the-art computer vision techniques perform worse than textual models in general tasks (accounting for semantic relatedness), they are as good or better models of the meaning of words with visual correlates such as color terms, even in a nontrivial task that involves nonliteral uses of such words. Moreover, we show that visual and textual information are tapping on different aspects of meaning, and indeed combining them in multimodal models often improves performance.

Ontology-based semantic similarity: A new feature-based approach

Abstract: Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.

Vector Space Models of Word Meaning and Phrase Meaning: A Survey

Abstract: Distributional models represent a word through the contexts in which it has been observed. They can be used to predict similarity in meaning, based on the distributional hypothesis, which states that two words that occur in similar contexts tend to have similar meanings. Distributional approaches are often implemented in vector space models. They represent a word as a point in high-dimensional space, where each dimension stands for a context item, and a word's coordinates represent its context counts. Occurrence in similar contexts then means proximity in space. In this survey we look at the use of vector space models to describe the meaning of words and phrases: the phenomena that vector space models address, and the techniques that they use to do so. Many word meaning phenomena can be described in terms of semantic similarity: synonymy, priming, categorization, and the typicality of a predicate's arguments. But vector space models can do more than just predict semantic similarity. They are a very flexible tool, because they can make use of all of linear algebra, with all its data structures and operations. The dimensions of a vector space can stand for many things: context words, or non-linguistic context like images, or properties of a concept. And vector space models can use matrices or higher-order arrays instead of vectors for representing more complex relationships. Polysemy is a tough problem for distributional approaches, as a representation that is learned from all of a word's contexts will conflate the different senses of the word. It can be addressed, using either clustering or vector combination techniques. Finally, we look at vector space models for phrases, which are usually constructed by combining word vectors. Vector space models for phrases can predict phrase similarity, and some argue that they can form the basis for a general-purpose representation framework for natural language semantics.

Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD

Abstract: In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.

Rotation-invariant similarity in time series using bag-of-patterns representation

Abstract: For more than a decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search relies on shape-based similarity matching. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We performed extensive experiments and show that our approach outperforms the leading existing methods in clustering, classification, and anomaly detection on dozens of real datasets. We further demonstrate that the representation allows rotation-invariant matching in shape datasets.

Large-scale learning of word relatedness with constraints

Abstract: Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.

UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures

Abstract: We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three metrics. It uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity. These range from simple character and word n-grams and common subsequences to complex features such as Explicit Semantic Analysis vector comparisons and aggregation of word similarity based on lexical-semantic resources. Further, we employ a lexical substitution system and statistical machine translation to add additional lexemes, which alleviates lexical gaps. Our final models, one per dataset, consist of a log-linear combination of about 20 features, out of the possible 300+ features implemented.

TakeLab: Systems for Measuring Semantic Text Similarity

Abstract: This paper describes the two systems for determining the semantic similarity of short texts submitted to the SemEval 2012 Task 6. Most of the research on semantic similarity of textual content focuses on large documents. However, a fair amount of information is condensed into short text snippets such as social media posts, image captions, and scientific abstracts. We predict the human ratings of sentence similarity using a support vector regression model with multiple features measuring word-overlap similarity and syntax similarity. Out of 89 systems submitted, our two systems ranked in the top 5, for the three overall evaluation metrics used (overall Pearson -- 2nd and 3rd, normalized Pearson -- 1st and 3rd, weighted mean -- 2nd and 5th).

KORE: keyphrase overlap relatedness for entity disambiguation

Abstract: Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end, we have developed a novel notion of semantic relatedness between two entities represented as sets of weighted (multi-word) keyphrases, with consideration of partially overlapping phrases. This measure improves the quality of prior link-based models, and also eliminates the need for (usually Wikipedia-centric) explicit interlinkage between entities. Thus, our method is more versatile and can cope with long-tail and newly emerging entities that have few or no links associated with them. For efficiency, we have developed approximation techniques based on min-hash sketches and locality-sensitive hashing. Our experiments on semantic relatedness and on named entity disambiguation demonstrate the superiority of our method compared to state-of-the-art baselines.

Semantic similarity analysis of protein data: assessment with biological features and issues

Abstract: The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.

The Semantic Web – ISWC 2012

Abstract: Research Track- MORe: Modular Combination of OWL Reasoners for Ontology Classification- A Formal Semantics for Weighted Ontology- Personalised Graph-Based Selection of Web APIs- Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing- Automatic Typing of DBpedia Entities- Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies- Concept-Based Semantic Difference in Expressive Description Logics- SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data- RDFS Reasoning on Massively Parallel Hardware- An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices- Semantic Enrichment by Non-experts: Usability of Manual Annotation Tools- Ontology-Based Access to Probabilistic Data with OWL QL- Predicting Reasoning Performance Using Ontology Metrics- Formal Verification of Data Provenance Records- Cost Based Query Ordering over OWL Ontologies- Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig- Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web- The Not-So-Easy Task of Computing Class Subsumptions in OWL RL- Strabon: A Semantic Geospatial DBMS- DeFacto - Deep Fact Validation- Feature LDA: A Supervised Topic Model for Automatic Detection of Web API Documentations from the Web- Efficient Execution of Top-K SPARQL Queries- Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy- Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures- Hitting the Sweetspot: Economic Rewriting of Knowledge Bases- Mining Semantic Relations between Research Areas- Discovering Concept Coverings in Ontologies of Linked Data Sources- Ontology Constraints in Incomplete and Complete Data- A Machine Learning Approach for Instance Matching Based on Similarity Metrics- Who Will Follow Whom? Exploiting Semantics for Link Prediction in Attention-Information Networks- On the Diversity and Availability of Temporal Information in Linked Open Data- Semantic Sentiment Analysis of Twitter- CrowdMap: Crowdsourcing Ontology Alignment with Microtasks- Domain-Aware Ontology Matching- Rapidly Integrating Services into the Linked Data Cloud- An Evidence-Based Verification Approach to Extract Entities and Relations for Knowledge Base Population- Blank Node Matching and RDF/S Comparison Functions- Hybrid SPARQL Queries: Fresh vs Fast Results- Provenance for SPARQL Queries- SRBench: A Streaming RDF/SPARQL Benchmark- Scalable Geo-thematic Query Answering- In-Use Track- Managing the Life-Cycle of Linked Data with the LOD2 Stack- Achieving Interoperability through Semantics-Based Technologies: The Instant Messaging Case- Linking Smart Cities Datasets with Human Computation - The Case of UrbanMatch- ourSpaces - Design and Deployment of a Semantic Virtual Research Environment- Embedded EL+ Reasoning on Programmable Logic Controllers- Experiences with Modeling Composite Phenotypes in the SKELETOME Project- Toward an Ecosystem of LOD in the Field: LOD Content Generation and Its Consuming Service- Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions- deqa: Deep Web Extraction for Question Answering- QuerioCity: A Linked Data Platform for Urban Information Management- Semantic Similarity-Driven Decision Support in the Skeletal Dysplasia Domain- Using SPARQL to Query BioPortal Ontologies and Metadata- Trentino Government Linked Open Geo-data: A Case Study- Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia- Query Driven Hypothesis Generation for Answering Queries over NLP Graphs- A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson- Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids- Evaluations and Experiments Track- Evaluating Semantic Search Query Approaches with Expert and Casual Users- Extracting Justifications from BioPortal Ontologies- Linked Stream Data Processing Engines: Facts and Figures- Benchmarking Federated SPARQL Query Engines: Are ExistingTestbeds Enough?- Tag Recommendation for Large-Scale Ontology-Based Information Systems- Evaluation of Techniques for Inconsistency Handling in OWL 2 QL Ontologies- Evaluating Entity Summarization Using a Game-Based Ground Truth- Evaluation of a Layered Approach to Question Answering over Linked Data- Doctoral Consortium - Long Papers- Cross Lingual Semantic Search by Improving Semantic Similarity and Relatedness Measures- Quality Reasoning in the Semantic Web- Burst the Filter Bubble: Using Semantic Web to Enable Serendipity- Reconstructing Provenance- Very Large Scale OWL Reasoning through Distributed Computation- Replication for Linked Data- Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources- Doctoral Consortium - Short Papers- Distributed Reasoning on Semantic Data Streams- Reusing XML Schemas' Information as a Foundation for Designing Domain Ontologies - A Multi-domain Framework for Community Building Based on Data Tagging- Towards a Theoretical Foundation for the Harmonization of Linked Data- Knowledge Pattern Extraction and Their Usage in Exploratory Search- SPARQL Update for Complex Event Processing- Online Unsupervised Coreference Resolution for Semi-structured Heterogeneous Data- Composition of Linked Data-Based RESTful Services

Weakly Supervised Training of Semantic Parsers

Abstract: We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, and syntactic supervision from dependency-parsed sentences. We apply our approach to train a semantic parser that uses 77 relations from Freebase in its knowledge representation. This semantic parser extracts instances of binary relations with state-of-the-art accuracy, while simultaneously recovering much richer semantic structures, such as conjunctions of multiple relations with partially shared arguments. We demonstrate recovery of this richer structure by extracting logical forms from natural language queries against Freebase. On this task, the trained semantic parser achieves 80% precision and 56% recall, despite never having seen an annotated logical form.

A Comparison of Greedy and Optimal Assessment of Natural Language Student Input Using Word-to-Word Similarity Metrics

Abstract: We present in this paper a novel, optimal semantic similarity approach based on word-to-word similarity metrics to solve the important task of assessing natural language student input in dialogue-based intelligent tutoring systems. The optimal matching is guaranteed using the sailor assignment problem, also known as the job assignment problem, a well-known combinatorial optimization problem. We compare the optimal matching method with a greedy method as well as with a baseline method on data sets from two intelligent tutoring systems, AutoTutor and iSTART.

Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms

Abstract: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology The task to assign a curator-reviewed function to every single sequence is impracticable Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score The algorithm is based on the original idea developed in a previous tool called Argot The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation The server is freely accessible at http://wwwmedcompmedicinaunipdit/Argot2

Neural Correlates of Creativity in Analogical Reasoning

Abstract: Brain-based evidence has implicated the frontal pole of the brain as important for analogical mapping. Separately, cognitive research has identified semantic distance as a key determinant of the creativity of analogical mapping (i.e., more distant analogies are generally more creative). Here, we used functional magnetic resonance imaging to assess brain activity during an analogy generation task in which we varied the semantic distance of analogical mapping (as derived quantitatively from a latent semantic analysis). Data indicated that activity within an a priori region of interest in left frontopolar cortex covaried parametrically with increasing semantic distance, even after removing effects of task difficulty. Results implicate increased recruitment of frontopolar cortex as a mechanism for integrating semantically distant information to generate solutions in creative analogical reasoning.

Methods and systems of supervised learning of semantic relatedness

Abstract: A method of evaluating a semantic relatedness of terms. The method comprises providing a plurality of text segments, calculating, using a processor, a plurality of weights each for another of the plurality of text segments, calculating a prevalence of a co-appearance of each of a plurality of pairs of terms in the plurality of text segments, and evaluating a semantic relatedness between members of each the pair according to a combination of a respective the prevalence and a weight of each of the plurality of text segments wherein a co-appearance of the pair occurs.

An abundance of riches: cross-task comparisons of semantic richness effects in visual word recognition

Abstract: There is considerable evidence (e.g., Pexman, Hargreaves, Siakaluk, Bodner, & Pope, 2008) that semantically rich words, which are associated with relatively more semantic information, are recognized faster across different lexical processing tasks. The present study extends this earlier work by providing the most comprehensive evaluation to date of semantic richness effects on visual word recognition performance. Specifically, using regression analyses to control for the influence of correlated lexical variables, we considered the impact of contextual dispersion, number of features, number of senses, semantic neighborhood density, imageability, and body-object interaction across five visual word recognition tasks: standard lexical decision, go/no-go lexical decision, speeded pronunciation, semantic classification, and progressive demasking. Semantic richness effects could be reliably detected in all tasks of lexical processing, indicating that semantic representations, particularly their imaginal and featural aspects, play a fundamental role in visual word recognition. However, there was also evidence that the strength of certain richness effects could be flexibly and adaptively modulated by task demands, consistent with an intriguing interplay between task-specific mechanisms and differentiated semantic processing.

AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Abstract: Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

Semantic-Gap-Oriented Active Learning for Multilabel Image Annotation

Abstract: User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivation of user feedback: to tackle the semantic gap. The size of the semantic gap of each concept is an important factor that affects the performance of user feedback. User should pay more efforts to the concepts with large semantic gaps, and vice versa. In this paper, we propose a semantic-gap-oriented active learning method, which incorporates the semantic gap measure into the information-minimization-based sample selection strategy. The basic learning model used in the active learning framework is an extended multilabel version of the sparse-graph-based semisupervised learning method that incorporates the semantic correlation. Extensive experiments conducted on two benchmark image data sets demonstrated the importance of bringing the semantic gap measure into the active learning process.

Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations

Abstract: Emotion detection from text is a relatively new classification task. This paper proposes a novel unsupervised context-based approach to detecting emotion from text at the sentence level. The proposed methodology does not depend on any existing manually crafted affect lexicons such as Word Net-Affect, thereby rendering our model flexible enough to classify sentences beyond Ekman's model of six basic emotions. Our method computes an emotion vector for each potential affect bearing word based on the semantic relatedness between words and various emotion concepts. The scores are then fine tuned using the syntactic dependencies within the sentence structure. Extensive evaluation on various data sets shows that our framework is a more generic and practical solution to the emotion classification problem and yields significantly more accurate results than recent unsupervised approaches.

The Role of Semantic Diversity in Lexical Organization

Abstract: Recent research has challenged the notion that word frequency is the organizing principle underlying lexical access, pointing instead to the number of contexts that a word occurs in (Adelman, Brown, & Quesada, 2006). Counting contexts gives a better quantitative fit to human lexical decision and naming data than counting raw occurrences of words. However, this approach ignores the information redundancy of the contexts in which the word occurs, a factor we refer to as semantic diversity. Using both a corpus-based study and a controlled artificial language experiment, we demonstrate the importance of contextual redundancy in lexical access, suggesting that contextual repetitions in language only increase a word’s memory strength if the repetitions are accompanied by a modulation in semantic context. We introduce a cognitive process mechanism to explain the pattern of behaviour by encoding the word’s context relative to the information redundancy between the current context and the word’s current memory representation. The model gives a better account of identification latency data than models based on either raw frequency or document count, and also produces a better-organized space to simulate semantic similarity.

Improving Multilingual Semantic Interoperation in Cross-Organizational Enterprise Systems Through Concept Disambiguation

Abstract: For the multilingual semantic interoperations in cross-organizational enterprise systems and e-commerce systems, semantic consistency is a research issue that has not been well resolved. This paper contributes to improving multilingual semantic interoperation by proposing a concept-connected near synonym (NSG) framework for concept disambiguation. NSG framework provides a vocabulary preprocessing process of collaborative vocabulary editing, which further ensures semantically consistent vocabulary for building semantically consistent business processes and documents between context-different information systems. The vocabulary preprocessing offered by NSG automates the process of finding potential near synonym sets and identifying collaboratively editable near synonym sets. The realization of NSG framework includes a probability model that computes concept values between concepts based on a newly introduced semantic relatedness method-SRCT. In this paper, SRCT-based methods are implemented and compared with some existing semantic relatedness methods. Experiments have shown that SRCT-based methods outperform the existing methods. This paper has made an improvement on the existing methods of semantic relatedness and reduces the collaboration cost of collaborative vocabulary editing.

An improved plagiarism detection scheme based on semantic role labeling

Abstract: Plagiarism occurs when the content is copied without permission or citation. One of the contributing factors is that many text documents on the internet are easily copied and accessed. This paper introduces a plagiarism detection technique based on the Semantic Role Labeling (SRL). The technique analyses and compares text based on the semantic allocation for each term inside the sentence. SRL is superior in generating arguments for each sentence semantically. Weighting for each argument generated by SRL to study its behaviour is also introduced in this paper. It was found that not all arguments affect the plagiarism detection process. In addition, experimental results on PAN-PC-09 data sets showed that our method significantly outperforms the modern methods for plagiarism detection in terms of Recall, Precision and F-measure.