http://dl.acm.org/citation.cfm?id=2560566 http://dl.acm.org/citation.cfm?id=2560566 Sun, 01 Dec 2013 00:00:00 GMT 2560566 http://dl.acm.org/citation.cfm?id=2556529
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Our aim is to build listening agents that attentively listen to their users and satisfy their desire to speak and have themselves heard. This article investigates how to automatically create a dialogue control component of such a listening agent. We collected a large number of listening-oriented dialogues with their user satisfaction ratings and used them to create a dialogue control component that satisfies users by means of Partially Observable Markov Decision Processes (POMDPs). Using a hybrid dialog controller where high-level dialog acts are chosen with a statistical policy and low-level slot values are populated by a wizard, we evaluated our dialogue control method in a Wizard-of-Oz experiment.]]> Fri, 03 Jan 2014 00:00:00 GMT 2513145 http://dl.acm.org/citation.cfm?id=2513563
To overcome the fact that the length of sentences is short and their content is limited, we regard words as independent text objects rather than features of sentences in sentence clustering and develop two co-clustering frameworks, namely integrated clustering and interactive clustering, to cluster sentences and words simultaneously. Since real-world datasets always contain noise, we incorporate noise detection and removal to enhance clustering of sentences and words. Meanwhile, a semisupervised approach is explored to incorporate the query information (and the sentence information in early document sets) in theme-based summarization. Thorough experimental studies are conducted. When evaluated on the DUC2005-2007 datasets and TAC 2008-2009 datasets, the performance of the two noise-detecting co-clustering approaches is comparable with that of the top three systems.]]> Fri, 03 Jan 2014 00:00:00 GMT 2513563 http://dl.acm.org/citation.cfm?id=2513146
Extracting semantic relations from text is a preliminary step towards understanding the meaning of text. The more semantic relations are extracted from a sentence, the better the representation of the knowledge encoded into that sentence. This article introduces a framework for the Composition of Semantic Relations (CSR). CSR aims to reveal more text semantics than existing semantic parsers by composing new relations out of previously extracted relations. Semantic relations are defined using vectors of semantic primitives, and an algebra is suggested to manipulate these vectors according to a CSR algorithm. Inference axioms that combine two relations and yield another relation are generated automatically.]]> Fri, 03 Jan 2014 00:00:00 GMT 2513146 http://dl.acm.org/citation.cfm?id=2513147
The search space of Phrase-Based Statistical Machine Translation (PBSMT) systems can be represented as a directed acyclic graph (lattice). By exploring this search space, it is possible to analyze and understand the failures of PBSMT systems. Indeed, useful diagnoses can be obtained by computing the so-called oracle hypotheses, which are hypotheses in the search space that have the highest quality score. For standard SMT metrics, this problem is, however, NP-hard and can only be solved approximately. In this work, we present two new methods for efficiently computing oracles on lattices: the first one is based on a linear approximation of the corpus bleu score and is solved using generic shortest distance algorithms; the second one relies on an Integer Linear Programming (ILP) formulation of the oracle decoding that incorporates count clipping constraints.]]> Fri, 03 Jan 2014 00:00:00 GMT 2513147 http://dl.acm.org/citation.cfm?id=2537046
This research presents a new benchmark dataset for evaluating Short Text Semantic Similarity (STSS) measurement algorithms and the methodology used for its creation. The power of the dataset is evaluated by using it to compare two established algorithms, STASIS and Latent Semantic Analysis. This dataset focuses on measures for use in Conversational Agents; other potential applications include email processing and data mining of social networks. Such applications involve integrating the STSS algorithm in a complex system, but STSS algorithms must be evaluated in their own right and compared with others for their effectiveness before systems integration. Semantic similarity is an artifact of human perception; therefore its evaluation is inherently empirical and requires benchmark datasets derived from human similarity ratings.]]> Fri, 03 Jan 2014 00:00:00 GMT 2537046 http://dl.acm.org/citation.cfm?id=2539053
Natural language search relies strongly on perceiving semantics in a query sentence. Semantics is captured by the relationship among the query words, represented as a network (graph). Such a network of words can be fed into larger ontologies, like DBpedia or Google Knowledge Graph, where they appear as subgraphs— fashioning the name subnetworks (subnets). Thus, subnet is a canonical form for interfacing a natural language query to a graph database and is an integral step for graph-based searching. In this article, we present a novel standalone NLP technique that leverages the cognitive psychology notion of semantic strata for semantic subnetwork extraction from natural language queries.]]> Fri, 03 Jan 2014 00:00:00 GMT 2539053