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Автор	Тема: На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:24 PART 6: NATURAL - LANGUAGE _ PROCESSING: 1500p[b] [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:24 PART-6: NATURAL-LANGUAGE PROCESSING: [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:25 PART-6: NATURAL-LANGUAGE PROCESSING: =251=> 1501p Knowledge-Based WSD on Specific Domains: performing Better than Generic Supervised WSD, Eneko Agirre, Oier Lopez de Lacalle, Aitor Soroa, http://ijcai.org/papers09/Abstracts/251.html This paper explores the application of knowledge-based Word Sense Disambiguation systems to specific domains, based on our state-of-the-art graph-based WSD system that uses the information in WordNet. Evaluation was performed over a publicly available domain-specific dataset of 41 words related to Sports and Finance, comprising examples drawn from three corpora: one balanced corpus (BNC), and two domain-specific corpora (news related to Sports and Finance). The results show that in all three corpora our knowledge-based WSD algorithm improves over previous results, and also over two state-of-the-art supervised WSD systems trained on SemCor, the largest publicly available annotated corpus. We also show that using related words as context, instead of the actual occurrence contexts, yields better results on the domain datasets, but not on the general one. Interestingly, the results are higher for domain-specific corpus than for the general corpus, raising prospects for improving current WSD systems when applied to specific domains. text: http://ijcai.org/papers09/Papers/IJCAI09-251.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =252=> 1507p Web-Scale N-gram Models for Lexical Disambiguation, Shane Bergsma, D. Lin, Randy Goebel, http://ijcai.org/papers09/Abstracts/252.html Web-scale data has been used in a diverse range of language research. Most of this research has used web counts for only short, fixed spans of context. We present a unified view of using web counts for lexical disambiguation. Unlike previous approaches, our supervised and unsupervised systems combine information from multiple and overlapping segments of context. On the tasks of preposition selection and context-sensitive spelling correction, the supervised system reduces disambiguation error by 20-24% over the current state-of-the-art. text: http://ijcai.org/papers09/Papers/IJCAI09-252.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:25 PART-6: NATURAL-LANGUAGE PROCESSING: -253=> 1513p Explicit Versus Latent Concept Models for Cross-Language Information Retrieval, Philipp Cimiano, A.Schultz, Sergej Sizov, Philipp Sorg, S.Staab, http://ijcai.org/papers09/Abstracts/253.html The field of information retrieval and text manipulation (classification, clustering) still strives for models allowing semantic information to be folded in to improve performance with respect to standard bag-of-word based models. Many approaches aim at a concept-based retrieval, but differ in the nature of the concepts, which range from linguistic concepts as defined in lexical resources such as WordNet, latent topics derived from the data itself—as in Latent Semantic Indexing (LSI) or (Latent Dirichlet Allocation (LDA)—to Wikipedia articles as proxies for concepts, as in the recently proposed Explicit Semantic Analysis (ESA) model. A crucial question which has not been answered so far is whether models based on explicitly given concepts (as in the ESA model for instance) perform inherently better than retrieval models based on "latent" concepts (as in LSI and/or LDA). In this paper we investigate this question closer in the context of a cross-language setting, which inherently requires concept-based retrieval bridging between different languages. In particular, we compare the recently proposed ESA model with two latent models (LSI and LDA) showing that the former is clearly superior to the both. From a general perspective, our results contribute to clarifying the role of explicit vs. implicitly derived or latent concepts in (cross-language) information retrieval research. text: http://ijcai.org/papers09/Papers/IJCAI09-253.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =254=> 1519p Detection of Imperative and Declarative Question-Answer Pairs in Email Conversations, Helen Kwong, Neil Yorke-Smith, http://ijcai.org/papers09/Abstracts/254.html Question-answer pairs extracted from email threads can help construct summaries of the thread, as well as inform semantic-based assistance with email. Previous work dedicated to email threads extracts only questions in interrogative form. We extend the scope of question and answer detection and pairing to encompass also questions in imperative and declarative forms, and to operate at sentence-level fidelity. Building on prior work, our methods are based on learned models over a set of features that include the content, context, and structure of email threads. For two large email corpora, we show that our methods balance precision and recall in extracting question-answer pairs, while maintaining a modest computation time. text: http://ijcai.org/papers09/Papers/IJCAI09-254.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:25 PART-6: NATURAL-LANGUAGE PROCESSING: =255=> 1525p Reading between the Lines, Loizos Michael, http://ijcai.org/papers09/Abstracts/255.html Reading involves, among others, identifying what is implied but not expressed in text. This task, known as textual entailment, offers a natural abstraction for many NLP tasks, and has been recognized as a central tool for the new area of Machine Reading. Important in the study of textual entailment is making precise the sense in which something is implied by text. The operational definition often employed is a subjective one: something is implied if humans are more likely to believe it given the truth of the text, than otherwise. In this work we propose a natural objective definition for textual entailment. Our approach is to view text as a partial depiction of some underlying hidden reality. Reality is mapped into text through a possibly stochastic process, the author of the text. Textual entailment is then formalized as the task of accurately, in a defined sense, recovering information about this hidden reality. We show how existing machine learning work can be applied to this information recovery setting, and discuss the implications for the construction of machines that autonomously engage in textual entailment. We then investigate the role of using multiple inference rules for this task. We establish that such rules cannot be learned and applied in parallel, but that layered learning and reasoning are necessary. text: http://ijcai.org/papers09/Papers/IJCAI09-255.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =256=> 1531p Improving Morphology Induction by Learning Spelling Rules, Jason Naradowsky, S. Goldwater, http://ijcai.org/papers09/Abstracts/256.html Unsupervised learning of morphology is an important task for human learners and in natural language processing systems. Previous systems focus on segmenting words into substrings (taking ⇒ tak.ing), but sometimes a segmentation-only analysis is insufficient (e.g., taking may be more appropriately analyzed as take+ing, with a spelling rule accounting for the deletion of the stem-final e). In this paper, we develop a Bayesian model for simultaneously inducing both morphology and spelling rules. We show that the addition of spelling rules improves performance over the baseline morphology-only model. text: http://ijcai.org/papers09/Papers/IJCAI09-256.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:25 PART-6: NATURAL-LANGUAGE PROCESSING: =257=> 1537p Improving a Virtual Human Using a Model of Degrees of Grounding, Antonio Roque, D. Traum, http://ijcai.org/papers09/Abstracts/257.html We describe the Degrees of Grounding model, which tracks the extent to which material has reached mutual belief in a dialogue, and conduct experiments in which the model is used to manage grounding behavior in spoken dialogues with a virtual human. We show that the model produces improvements in virtual human performance as measured by post-session questionnaires. text: http://ijcai.org/papers09/Papers/IJCAI09-257.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =258=> 1543p On the Tip of My Thought: Playing the Guillotine Game, Giovanni Semeraro, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, http://ijcai.org/papers09/Abstracts/258.html In this paper we propose a system to solve a language game, called Guillotine, which requires a player with a strong cultural and linguistic background knowledge. The player observes a set of five words, generally unrelated to each other, and in one minute she has to provide a sixth word, semantically connected to the others. Several knowledge sources, such as a dictionary and a set of proverbs, have been modeled and integrated in order to realize a knowledge infusion process into the system. The main motivation for designing an artificial player for Guillotine is the challenge of providing the machine with the cultural and linguistic background knowledge which makes it similar to a human being, with the ability of interpreting natural language documents and reasoning on their content. Experiments carried out showed promising results, and both the knowledge source modeling and the reasoning mechanisms (implementing a spreading activation algorithm to find out the solution) seem to be appropriate. We are convinced that the approach has a great potential for other more practical applications besides solving a language game, such as semantic search. text: http://ijcai.org/papers09/Papers/IJCAI09-258.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:25 PART-6: NATURAL-LANGUAGE PROCESSING: =259=> 1549p Introspection & Adaptable Model Integration for Dialogue-based Question Answering (Q&A), Daniel Sonntag, http://ijcai.org/papers09/Abstracts/259.html Dialogue-based Question Answering (QA) is a highly complex task that brings together a QA system including various natural language processing components (i.e., components for question classification, information extraction, and retrieval) with dialogue systems for effective and natural communication. The dialogue-based access is difficult to establish when the QA system in use is complex and combines many different answer services with different quality and access characteristics. For example, some questions are processed by opendomain QA services with a broad coverage. Others should be processed by using a domain-specific instance ontology for more reliable answers. Different answer services may change their characteristics over time and the dialogue reaction models have to be updated according to that. To solve this problem, we developed introspective methods to integrate adaptable models of the answer services. We evaluated the impact of the learned models on the dialogue performance, i.e., whether the adaptable models can be used for a more convenient dialogue formulation process. We show significant effectiveness improvements in the resulting dialogues when using the machine learning (ML) models. Examples are provided in the context of the generation of system-initiative feedback to user questions and answers, as provided by heterogeneous information services. text: http://ijcai.org/papers09/Papers/IJCAI09-259.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =260=> 1555p Context-Based Approach for Pivot Translation Services, Rie Tanaka, Y. Murakami, Toru Ishida, http://ijcai.org/papers09/Abstracts/260.html Machine translation services available on the Web are becoming increasingly popular. However, a pivot translation service is required to realize translations between non-English languages by cascading different translation services via English. As a result, the meaning of words often drifts due to the inconsistency, asymmetry and intransitivity of word selections among translation services. In this paper, we propose context-based coordination to maintain the consistency of word meanings during pivot translation services. First, we propose a method to automatically generate multilingual equivalent terms based on bilingual dictionaries and use generated terms to propagate context among combined translation services. Second, we show a multiagent architecture as one way of implementation, wherein a coordinator agent gathers and propagates context from/to a translation agent. We generated trilingual equivalent noun terms and implemented a Japanese-to-German-and-back translation, cascading into four translation services. The evaluation results showed that the generated terms can cover over 58% of all nouns. The translation quality was improved by 40% for all sentences, and the quality rating for all sentences increased by an average of 0.47 points on a five-point scale. These results indicate that we can realize consistent pivot translation services through context-based coordination based on existing services. text: http://ijcai.org/papers09/Papers/IJCAI09-260.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:25 PART-6: NATURAL-LANGUAGE PROCESSING: =261=> 1562p Online Graph Planarisation for Synchronous Parsing of Semantic & Syntactic Dependencies, Ivan Titov, James Henderson, Paola Merlo, Gabriele Musillo, http://ijcai.org/papers09/Abstracts/261.html This paper investigates a generative history-based parsing model that synchronises the derivation of non-planar graphs representing semantic dependencies with the derivation of dependency trees representing syntactic structures. To process non-planarity online, the semantic transition-based parser uses a new technique to dynamically reorder nodes during the derivation. While the synchronised derivations allow different structures to be built for the semantic non-planar graphs and syntactic dependency trees, useful statistical dependencies between these structures are modeled using latent variables. The resulting synchronous parser achieves competitive performance on the CoNLL-2008 shared task, achieving relative error reduction of 12% in semantic F score over previously proposed synchronous models that cannot process non-planarity online. text: http://ijcai.org/papers09/Papers/IJCAI09-261.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =262=> 1568p Computational Semantics of Noun Compounds in a Semantic Space Model, Akira Utsumi, http://ijcai.org/papers09/Abstracts/262.html This study examines the ability of a semantic space model to represent the meaning of noun compounds such as "information gathering" or "weather forecast," A new algorithm, comparison, is proposed for computing compound vectors from constituent word vectors, and compared with other algorithms (i.e., predication and centroid) in terms of accuracy of multiple-choice synonym test and similarity judgment test. The result of both tests is that the comparison algorithm is, on the whole, superior to other algorithms, and in particular achieves the best performance when noun compounds have emergent meanings. Furthermore, the comparison algorithm also works for novel noun compounds that do not occur in the corpus. These findings indicate that a semantic space model in general and the comparison algorithm in particular has sufficient ability to compute the meaning of noun compounds. text: http://ijcai.org/papers09/Papers/IJCAI09-262.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:26 PART-6: NATURAL-LANGUAGE PROCESSING: =263=> 1574p Probabilistic Counting with Randomized Storage, Benjamin Van Durme, Ashwin Lall, http://ijcai.org/papers09/Abstracts/263.html Previous work by Talbot and Osborne [2007] explored the use of randomized storage mechanisms in language modeling. These structures trade a small amount of error for significant space savings, enabling the use of larger language models on relatively modest hardware. Going beyond space efficient count storage, here we present the Talbot Osborne Morris Bloom (TOMB) Counter, an extended model for performing space efficient counting over streams of finite length. Theoretical and experimental results are given, showing the promise of approximate counting over large vocabularies in the context of limited space. text: http://ijcai.org/papers09/Papers/IJCAI09-263.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =264=> 1580p Context-Sensitive Semantic Smoothing Using Semantically Relatable Sequences, Kamaljeet S. Verma, Pushpak Bhattacharyya, http://ijcai.org/papers09/Abstracts/264.html We propose a novel approach to context sensitive semantic smoothing by making use of an intermediate, "semantically light" representation for sentences, called Semantically Relatable Sequences (SRS). SRSs of a sentence are tuples of words appearing in the semantic graph of the sentence as linked nodes depicting dependency relations. In contrast to patterns based on consecutive words, SRSs make use of groupings of non-consecutive but semantically related words. Our experiments on TREC AP89 collection show that the mixture model of SRS translation model and Two Stage Language Model (TSLM) of Lafferty and Zhai achieves MAP scores better than the mixture model of MultiWord Expression (MWE) translation model and TSLM. Furthermore, a system, which for each test query selects either the SRS or the MWE mixture model based on better query MAP score, shows significant improvements over the individual mixture models. text: http://ijcai.org/papers09/Papers/IJCAI09-264.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:26 PART-6: NATURAL-LANGUAGE PROCESSING: =265=> 1586p Graph-Based Multi-Modality Learning for Topic-Focused Multi-Document Summarization, Xiaojun Wan, Jianguo Xiao, http://ijcai.org/papers09/Abstracts/265.html Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization. This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and the cross-document sentence relationships as two separate modalities (graphs). Three different fusion schemes, namely linear form, sequential form and score combination form, are exploited in the algorithm. Experimental results on the DUC benchmark datasets demonstrate the effectiveness of the proposed multi-modality learning algorithms with all the three fusion schemes. text: http://ijcai.org/papers09/Papers/IJCAI09-265.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =266=> 1592p Multiscale Analysis of Document Corpora Based on Diffusion Models, Chang Wang, Sridhar Mahadevan, http://ijcai.org/papers09/Abstracts/266.html We introduce a nonparametric approach to multiscale analysis of document corpora using a hierarchical matrix analysis framework called diffusion wavelets. In contrast to eigenvector methods, diffusion wavelets construct multiscale basis functions. In this framework, a hierarchy is automatically constructed by an iterative series of dilation and orthogonalization steps beginning with an initial set of orthogonal basis functions, such as the unit-vector bases. Each set of basis functions at a given level is constructed from the bases at the lower level by dilation using the dyadic powers of a diffusion operator. A novel aspect of our work is that the diffusion analysis is conducted on the space of variables (words), instead of instances (documents). This approach can automatically and efficiently determine the number of levels of the topical hierarchy, as well as the topics at each level. Multiscale analysis of document corpora is achieved by using the projections of the documents onto the spaces spanned by basis functions at different levels. Further, when the input term-term matrix is a local diffusion operator, the algorithm runs in time approximately linear in the number of non-zero elements of the matrix. The approach is illustrated on various data sets including NIPS conference papers, 20 Newsgroups and TDT2 data. text: http://ijcai.org/papers09/Papers/IJCAI09-266.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:26 PART-6: NATURAL-LANGUAGE PROCESSING: =267=> 1598p Wikispeedia: An Online Game for Inferring Semantic Distances between Concepts, Robert West, Joelle Pineau, Doina Precup, http://ijcai.org/papers09/Abstracts/267.html Computing the semantic distance between real-world concepts is crucial for many intelligent applications. We present a novel method that leverages data from `Wikispeedia', an online game played on Wikipedia; players have to reach an article from another, unrelated article, only by clicking links in the articles encountered. In order to automatically infer semantic distances between everyday concepts, our method effectively extracts the common sense displayed by humans during play, and is thus more desirable, from a cognitive point of view, than purely corpus-based methods. We show that our method significantly outperforms Latent Semantic Analysis in a psychometric evaluation of the quality of learned semantic distances. text: http://ijcai.org/papers09/Papers/IJCAI09-267.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: =268=> 1604p Situated Resolution and Generation of Spatial Referring Expressions for Robotic Assistants, Hendrik Zender, Geert-Jan M. Kruijff, Ivana Kruijff-Korbayová, http://ijcai.org/papers09/Abstracts/268.html In this paper we present an approach to the task of generating and resolving referring expressions (REs) for conversational mobile robots. It is based on a spatial knowledge base encompassing both robot- and human-centric representations. Existing algorithms for the generation of referring expressions (GRE) try to find a description that uniquely identifies the referent with respect to other entities that are in the current context. Mobile robots, however, act in large-scale space, that is environments that are larger than what can be perceived at a glance, e.g. an office building with different floors, each containing several rooms and objects. One challenge when referring to elsewhere is thus to include enough information so that the interlocutors can extend their context appropriately. We address this challenge with a method for context construction that can be used for both generating and resolving REs -- two previously disjoint aspects. Our approach is embedded in a bi-directional framework for natural language processing for robots. text: http://ijcai.org/papers09/Papers/IJCAI09-268.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:26 PART-6: NATURAL-LANGUAGE PROCESSING: =269=> 1610p On-line Evolutionary Exponential Family Mixture, Jianwen Zhang, Y.Song, Gang Chen, C.Zhang, http://ijcai.org/papers09/Abstracts/269.html This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e.,historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks. text: http://ijcai.org/papers09/Papers/IJCAI09-269.pdf ====================================== PART-6: NATURAL-LANGUAGE PROCESSING: -270=> 1616p Word Sense Disambiguation for All Words Without Hard Labor, Zhi Zhong, Hwee Tou Ng, http://ijcai.org/papers09/Abstracts/270.html While the most accurate word sense disambiguation systems are built using supervised learning from sense-tagged data, scaling them up to all words of a language has proved elusive, since preparing a sense-tagged corpus for all words of a language is time-consuming and human labor intensive. In this paper, we propose and implement a completely automatic approach to scale up word sense disambiguation to all words of English. Our approach relies on English-Chinese parallel corpora, English-Chinese bilingual dictionaries, and automatic methods of finding synonyms of Chinese words. No additional human sense annotations or word translations are needed. We conducted a large-scale empirical evaluation on more than 29,000 noun tokens in English texts annotated in OntoNotes 2.0, based on its coarse-grained sense inventory. The evaluation results show that our approach is able to achieve high accuracy, outperforming the first-sense baseline and coming close to a prior reported approach that requires manual human efforts to provide Chinese translations of English senses. text: http://ijcai.org/papers09/Papers/IJCAI09-270.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:26 PART-6: NATURAL-LANGUAGE PROCESSING... [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:27 PART-6: NATURAL-LANGUAGE PROCESSING... [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 21 авг 09 7:27 [Ответ][Цитата]
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