Semantic similarity

About: Semantic similarity is a research topic. Over the lifetime, 14605 publications have been published within this topic receiving 364659 citations. The topic is also known as: semantic relatedness.

Zero-Shot Visual Recognition via Bidirectional Latent Embedding

Abstract: Zero-shot learning for visual recognition, e.g., object and action recognition, has recently attracted a lot of attention. However, it still remains challenging in bridging the semantic gap between visual features and their underlying semantics and transferring knowledge to semantic categories unseen during learning. Unlike most of the existing zero-shot visual recognition methods, we propose a stagewise bidirectional latent embedding framework to two subsequent learning stages for zero-shot visual recognition. In the bottom-up stage, a latent embedding space is first created by exploring the topological and labeling information underlying training data of known classes via a proper supervised subspace learning algorithm and the latent embedding of training data are used to form landmarks that guide embedding semantics underlying unseen classes into this learned latent space. In the top-down stage, semantic representations of unseen-class labels in a given label vocabulary are then embedded to the same latent space to preserve the semantic relatedness between all different classes via our proposed semi-supervised Sammon mapping with the guidance of landmarks. Thus, the resultant latent embedding space allows for predicting the label of a test instance with a simple nearest-neighbor rule. To evaluate the effectiveness of the proposed framework, we have conducted extensive experiments on four benchmark datasets in object and action recognition, i.e., AwA, CUB-200-2011, UCF101 and HMDB51. The experimental results under comparative studies demonstrate that our proposed approach yields the state-of-the-art performance under inductive and transductive settings.

A Semantic Subsystem of Visual Attributes

Abstract: We propose that the many different forms of selective semantic impairment that have been reported over the past 20 years may be classified into three general classes: semantic-category selective, modality-of-input selective, and semantic-attribute selective. Particular patients may exhibit more than one form of selectivity, i.e. there can be doubly and perhaps even triply selective semantic impairments. We then describe a patient with a singly selective semantic impairment of a form not previously described: he was unable to access visual semantic attributes in semantic memory, whereas he could access semantic attributes relevant to other sensory modalities, and could also access non-perceptual semantic attributes. This pattern of results was independent both of modality of input and of semantic category of probed item. We infer from these data the existence of a semantic subsystem specific to the storage of information about visual attributes of animate and inanimate objects. An ERP study of sem...

Word Embeddings as Metric Recovery in Semantic Spaces

Abstract: Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and demonstrates that existing algorithms are consistent metric recovery methods given co-occurrence counts from random walks. Furthermore, we propose a simple, principled, direct metric recovery algorithm that performs on par with the state-of-the-art word embedding and manifold learning methods. Finally, we complement recent focus on analogies by constructing two new inductive reasoning datasets---series completion and classification---and demonstrate that word embeddings can be used to solve them as well.

Natural language processing : IJCNLP 2005 : Second International Joint Conference, Jeju Island, Korea, October 11-13, 2005 : proceedings

Abstract: Information Retrieval.- A New Method for Sentiment Classification in Text Retrieval.- Topic Tracking Based on Linguistic Features.- The Use of Monolingual Context Vectors for Missing Translations in Cross-Language Information Retrieval.- Automatic Image Annotation Using Maximum Entropy Model.- Corpus-Based Parsing.- Corpus-Based Analysis of Japanese Relative Clause Constructions.- Parsing Biomedical Literature.- Parsing the Penn Chinese Treebank with Semantic Knowledge.- Using a Partially Annotated Corpus to Build a Dependency Parser for Japanese.- Web Mining.- Entropy as an Indicator of Context Boundaries: An Experiment Using a Web Search Engine.- Automatic Discovery of Attribute Words from Web Documents.- Aligning Needles in a Haystack: Paraphrase Acquisition Across the Web.- Confirmed Knowledge Acquisition Using Mails Posted to a Mailing List.- Rule-Based Parsing.- Automatic Partial Parsing Rule Acquisition Using Decision Tree Induction.- Chunking Using Conditional Random Fields in Korean Texts.- High Efficiency Realization for a Wide-Coverage Unification Grammar.- Linguistically-Motivated Grammar Extraction, Generalization and Adaptation.- Disambiguation.- PP-Attachment Disambiguation Boosted by a Gigantic Volume of Unambiguous Examples.- Adapting a Probabilistic Disambiguation Model of an HPSG Parser to a New Domain.- A Hybrid Approach to Single and Multiple PP Attachment Using WordNet.- Period Disambiguation with Maxent Model.- Text Mining.- Acquiring Synonyms from Monolingual Comparable Texts.- A Method of Recognizing Entity and Relation.- Inversion Transduction Grammar Constraints for Mining Parallel Sentences from Quasi-Comparable Corpora.- Automatic Term Extraction Based on Perplexity of Compound Words.- Document Analysis.- Document Clustering with Grouping and Chaining Algorithms.- Using Multiple Discriminant Analysis Approach for Linear Text Segmentation.- Classifying Chinese Texts in Two Steps.- Assigning Polarity Scores to Reviews Using Machine Learning Techniques.- Ontology and Thesaurus.- Analogy as Functional Recategorization: Abstraction with HowNet Semantics.- PLSI Utilization for Automatic Thesaurus Construction.- Analysis of an Iterative Algorithm for Term-Based Ontology Alignment.- Finding Taxonomical Relation from an MRD for Thesaurus Extension.- Relation Extraction.- Relation Extraction Using Support Vector Machine.- Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-Based Clustering.- Automatic Relation Extraction with Model Order Selection and Discriminative Label Identification.- Mining Inter-Entity Semantic Relations Using Improved Transductive Learning.- Text Classification.- A Preliminary Work on Classifying Time Granularities of Temporal Questions.- Classification of Multiple-Sentence Questions.- Transliteration.- A Rule Based Syllabification Algorithm for Sinhala.- An Ensemble of Grapheme and Phoneme for Machine Transliteration.- Machine Translation - I.- Improving Statistical Word Alignment with Ensemble Methods.- Empirical Study of Utilizing Morph-Syntactic Information in SMT.- Question Answering.- Instance-Based Generation for Interactive Restricted Domain Question Answering Systems.- Answering Definition Questions Using Web Knowledge Bases.- Exploring Syntactic Relation Patterns for Question Answering.- Web-Based Unsupervised Learning for Query Formulation in Question Answering.- Morphological Analysis.- A Chunking Strategy Towards Unknown Word Detection in Chinese Word Segmentation.- A Lexicon-Constrained Character Model for Chinese Morphological Analysis.- Relative Compositionality of Multi-word Expressions: A Study of Verb-Noun (V-N) Collocations.- Automatic Extraction of Fixed Multiword Expressions.- Machine Translation - II.- Phrase-Based Statistical Machine Translation: A Level of Detail Approach.- Why Is Zero Marking Important in Korean?.- A Phrase-Based Context-Dependent Joint Probability Model for Named Entity Translation.- Machine Translation Based on Constraint-Based Synchronous Grammar.- Text Summarization.- A Machine Learning Approach to Sentence Ordering for Multidocument Summarization and Its Evaluation.- Significant Sentence Extraction by Euclidean Distance Based on Singular Value Decomposition.- Named Entity Recognition.- Two-Phase Biomedical Named Entity Recognition Using A Hybrid Method.- Heuristic Methods for Reducing Errors of Geographic Named Entities Learned by Bootstrapping.- Linguistic Resources and Tools.- Building a Japanese-Chinese Dictionary Using Kanji/Hanzi Conversion.- Automatic Acquisition of Basic Katakana Lexicon from a Given Corpus.- CTEMP: A Chinese Temporal Parser for Extracting and Normalizing Temporal Information.- French-English Terminology Extraction from Comparable Corpora.- Discourse Analysis.- A Twin-Candidate Model of Coreference Resolution with Non-Anaphor Identification Capability.- Improving Korean Speech Acts Analysis by Using Shrinkage and Discourse Stack.- Anaphora Resolution for Biomedical Literature by Exploiting Multiple Resources.- Automatic Slide Generation Based on Discourse Structure Analysis.- Semantic Analysis - I.- Using the Structure of a Conceptual Network in Computing Semantic Relatedness.- Semantic Role Labelling of Prepositional Phrases.- Global Path-Based Refinement of Noisy Graphs Applied to Verb Semantics.- Semantic Role Tagging for Chinese at the Lexical Level.- NLP Applications.- Detecting Article Errors Based on the Mass Count Distinction.- Principles of Non-stationary Hidden Markov Model and Its Applications to Sequence Labeling Task.- Integrating Punctuation Rules and Naive Bayesian Model for Chinese Creation Title Recognition.- A Connectionist Model of Anticipation in Visual Worlds.- Tagging.- Automatically Inducing a Part-of-Speech Tagger by Projecting from Multiple Source Languages Across Aligned Corpora.- The Verbal Entries and Their Description in a Grammatical Information-Dictionary of Contemporary Tibetan.- Tense Tagging for Verbs in Cross-Lingual Context: A Case Study.- Regularisation Techniques for Conditional Random Fields: Parameterised Versus Parameter-Free.- Semantic Analysis - II.- Exploiting Lexical Conceptual Structure for Paraphrase Generation.- Word Sense Disambiguation by Relative Selection.- Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features.- Automatic Interpretation of Noun Compounds Using WordNet Similarity.- Language Models.- An Empirical Study on Language Model Adaptation Using a Metric of Domain Similarity.- A Comparative Study of Language Models for Book and Author Recognition.- Spoken Language.- Lexical Choice via Topic Adaptation for Paraphrasing Written Language to Spoken Language.- A Case-Based Reasoning Approach for Speech Corpus Generation.- Terminology Mining.- Web-Based Terminology Translation Mining.- Extracting Terminologically Relevant Collocations in the Translation of Chinese Monograph.

Element Level Semantic Matching

Abstract: We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. The matching process is essentially divided into two steps: element level and structure level. Element level matchers consider only labels of nodes, while structure level matchers start from this information to consider the full graph. In this paper we present various element level semantic matchers, and discuss their implementation within the S-Match system. The main novelty of our approach is in that element level semantic matchers return semantic relations (=, E, I, ^) between concepts rather than similarity coefficients between labels in the [0, 1] range.