This investigation proposes an approach to modeling the discourse of spoken dialogue using semantic dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user’s utterance using the semantic dependency graphs.
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Ontological Framework for Retrieving Environmental Sounds Using Semantics and Acoustic Content
In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a ﬁrst attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers. ...
The Automatic Content Linking Device is a just-in-time document retrieval system which monitors an ongoing conversation or a monologue and enriches it with potentially related documents, including multimedia ones, from local repositories or from the Internet. The documents are found using keyword-based search or using a semantic similarity measure between documents and the words obtained from automatic speech recognition.
The limited coverage of lexical-semantic resources is a signiﬁcant problem for NLP systems which can be alleviated by automatically classifying the unknown words. Supersense tagging assigns unknown nouns one of 26 broad semantic categories used by lexicographers to organise their manual insertion into W ORD N ET. Ciaramita and Johnson (2003) present a tagger which uses synonym set glosses as annotated training examples. We describe an unsupervised approach, based on vector-space similarity, which does not require annotated examples but signiﬁcantly outperforms their tagger. ...
This paper describes a heuristic-based approach t o word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses, each heuristic draws on semantic relations extracted from a Webster's dictionary and the semantic thesaurus WordNet.
This paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval. We tackle a frequently-occurring class of questions that takes the form “What is the best drug treatment for X?” Starting from an initial set of MEDLINE citations, our system ﬁrst identiﬁes the drugs under study. Abstracts are then clustered using semantic classes from the UMLS ontology. Finally, a short extractive summary is generated for each abstract to populate the clusters. ...
Semantic relatedness is a very important factor for the coreference resolution task. To obtain this semantic information, corpusbased approaches commonly leverage patterns that can express a speciﬁc semantic relation. The patterns, however, are designed manually and thus are not necessarily the most effective ones in terms of accuracy and breadth. To deal with this problem, in this paper we propose an approach that can automatically ﬁnd the effective patterns for coreference resolution.
This paper examines whether a learningbased coreference resolver can be improved using semantic class knowledge that is automatically acquired from a version of the Penn Treebank in which the noun phrases are labeled with their semantic classes. Experiments on the ACE test data show that a resolver that employs such induced semantic class knowledge yields a statistically significant improvement of 2% in F-measure over one that exploits heuristically computed semantic class knowledge. In addition, the induced knowledge improves the accuracy of common noun resolution by 2-6%.
1 Efficient natural language generation has been successfully demonstrated using highly compiled knowledge about speech acts and their related social actions. A design and prototype implementation of a parser which utilizes this same pragmatic knowledge to efficiently guide parsing is presented. Such guidance is shown to prune the search space and thus avoid needless processing of pragmatically unlikely constituent structures.
This book is about more than just Microsoft Expression Web. For most people, a web editor
is a means to an end—that end being a website that establishes a web presence. My goal in
writing this book is for you to be able to use Expression Web to do more than just establish a
presence: my hope is that you will be able to use Expression Web to create a website that fulfills
the site owner’s goals.
Mourad Ouzzani Qatar Computing Research Institute Qatar Foundation Doha, Qatar firstname.lastname@example.org
Athman Bouguettaya School of Computer Science and Information Technology RMIT University Melbourne Victoria Australia email@example.com
Library of Congress Control Number: 2011939473 c Springer Science+Business Media, LLC 2011 All rights reserved.
As semantic technologies prove their value with targeted applications, there are increasing opportunities
to consider their application in social contexts for knowledge, learning, and human development.
Semantic Web and Knowledge Management has been accepted as a critical enabler aiming to in
crease knowledge-related performance by better use of intellectual assets, in addition to which many
governments are forced to increasingly deal with knowledge services that form larger parts of the global
economy and society.
Semantics is the study of the “toolkit” for meaning: knowledge encoded in the vocabulary of the language and in its patterns for building more elaborate meanings, up to the level of sentence meanings. Pragmatics is concerned with the use of these tools in meaningful communication. Pragmatics is about the interaction of semantic knowledge with our knowledge of the world, taking into account contexts of use. The following is part 2 of the ebook "An introduction to English Semantics and Pragmatics", inviting you to refer.
This paper presents a detailed study of the integration of knowledge from both dependency parses and hierarchical word ontologies into a maximum-entropy-based tagging model that simultaneously labels words with both syntax and semantics. Our ﬁndings show that information from both these sources can lead to strong improvements in overall system accuracy: dependency knowledge improved performance over all classes of word, and knowledge of the position of a word in an ontological hierarchy increased accuracy for words not seen in the training data. ...
This paper presents an open-domain textual Question-Answering system that uses several feedback loops to enhance its performance. These feedback loops combine in a new way statistical results with syntactic, semantic or pragmatic information derived from texts and lexical databases. The paper presents the contribution of each feedback loop to the overall performance of 76% human-assessed precise answers.
We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. ...
Our thesis shows the quality of semantic vector representation with random projection and Hyperspace Analogue to Language model under about the researching on Vietnamese. The main goal is how to find semantic similarity or to study synonyms in Vietnamese. We are also interested in the stability of our approach that uses Random Indexing and HAL to represent semantic of words or documents. We build a system to find the synonyms in Vietnamese called Semantic Similarity Finding System. In particular, we also evaluate synonyms resulted from our system.
am delighted to see a book on multimedia semantics covering metadata, analysis, and
interaction edited by three very active researchers in the field: Troncy, Huet, and Schenk.
This is one of those projects that are very difficult to complete because the field is
advancing rapidly in many different dimensions. At any time, you feel that many important
emerging areas may not be covered well unless you see the next important conference in
the field. A state of the art book remains a moving, often elusive, target. But this is only a
part of the dilemma.
To obtain the content summary of a database, a
metasearcher could rely on the database to supply the
summary (e.g., by following a protocol like STARTS ,
or possibly using Semantic Web  tags in the future).
Unfortunately many web-accessible text databases are
completely autonomous and do not report any detailed
metadata about their contents to facilitate metasearch-
ing. To handle such databases, a metasearcher could rely
on manually generated descriptions of the database con-