Encoding Classifications as Lightweight Ontologies

Classifications have been used for centuries with the goal of cataloguing and searching large sets of objects. In the early days it was mainly books; lately it has also become Web pages, pictures and any kind of electronic information items. Classifications describe their contents using natural language labels, which has proved very effective in manual classification. However natural language labels show their limitations when one tries to automate the process, as they make it very hard to reason about classifications and their contents. In this paper we introduce the novel notion of Formal Classification, as a graph structure where labels are written in a propositional concept language. Formal Classifications turn out to be some form of lightweight ontologies. This, in turn, allows us to reason about them, to associate to each node a normal form formula which univocally describes its contents, and to reduce document classification to reasoning about subsumption

Investigating the timecourse of accessing conversational implicatures during incremental sentence interpretation

Many contextual inferences in utterance interpretation are explained as following from the nature of conversation and the assumption that participants are rational. Recent psycholinguistic research has focussed on certain of these ‘Gricean’ inferences and have revealed that comprehenders can access them in online interpretation. However there have been mixed results as to the time-course of access. Some results show that Gricean inferences can be accessed very rapidly, as rapidly as any other contextually specified information (Sedivy, 2003; Grodner, Klein, Carbery, & Tanenhaus, 2010); while other studies looking at the same kind of inference suggest that access to Gricean inferences are delayed relative to other aspects of semantic interpretation (Huang & Snedeker, 2009; in press). While previous timecourse research has focussed on Gricean inferences that support the online assignment of reference to definite expressions, the study reported here examines the timecourse of access to scalar implicatures, which enrich the meaning of an utterance beyond the semantic interpretation. Even if access to Gricean inference in support of reference assignment may be rapid, it is still unknown whether genuinely enriching scalar implicatures are delayed. Our results indicate that scalar implicatures are accessed as rapidly as other contextual inferences. The implications of our results are discussed in reference to the architecture of language comprehension

The processing of ambiguous sentences by first and second language learners of English

This study compares the way English-speaking children and adult second language learners of English resolve relative clause attachment ambiguities in sentences such as The dean liked the secretary of the professor who was reading a letter. Two groups of advanced L2 learners of English with Greek or German as their L1 participated in a set of off-line and on-line tasks. While the participants ' disambiguation preferences were influenced by lexical-semantic properties of the preposition linking the two potential antecedent NPs (of vs. with), there was no evidence that they were applying any structure-based ambiguity resolution strategies of the type that have been claimed to influence sentence processing in monolingual adults. These findings differ markedly from those obtained from 6 to 7 yearold monolingual English children in a parallel auditory study (Felser, Marinis, & Clahsen, submitted) in that the children's attachment preferences were not affected by the type of preposition at all. We argue that whereas children primarily rely on structure-based parsing principles during processing, adult L2 learners are guided mainly by non-structural informatio

Inducing Constraint Grammars

Constraint Grammar rules are induced from corpora. A simple scheme based on local information, i.e., on lexical biases and next-neighbour contexts, extended through the use of barriers, reached 87.3 percent precision (1.12 tags/word) at 98.2 percent recall. The results compare favourably with other methods that are used for similar tasks although they are by no means as good as the results achieved using the original hand-written rules developed over several years time.Comment: 10 pages, uuencoded, gzipped PostScrip

Disambiguating past events: accurate source memory for time and context depends on different retrieval processes

Participant payment was provided by the School of Psychology and Neuroscience ResPay scheme.Current animal models of episodic memory are usually based on demonstrating integrated memory for what happened, where it happened, and when an event took place. These models aim to capture the testable features of the definition of human episodic memory which stresses the temporal component of the memory as a unique piece of source information that allows us to disambiguate one memory from another. Recently though, it has been suggested that a more accurate model of human episodic memory would include contextual rather than temporal source information, as humans’ memory for time is relatively poor. Here, two experiments were carried out investigating human memory for temporal and contextual source information, along with the underlying dual process retrieval processes, using an immersive virtual environment paired with a ‘Remember-Know’ memory task. Experiment 1 (n = 28) showed that contextual information could only be retrieved accurately using recollection, while temporal information could be retrieved using either recollection or familiarity. Experiment 2 (n = 24), which used a more difficult task, resulting in reduced item recognition rates and therefore less potential for contamination by ceiling effects, replicated the pattern of results from Experiment 1. Dual process theory predicts that it should only be possible to retrieve source context from an event using recollection, and our results are consistent with this prediction. That temporal information can be retrieved using familiarity alone suggests that it may be incorrect to view temporal context as analogous to other typically used source contexts. This latter finding supports the alternative proposal that time since presentation may simply be reflected in the strength of memory trace at retrieval – a measure ideally suited to trace strength interrogation using familiarity, as is typically conceptualised within the dual process framework.PostprintPeer reviewe

Speech perception under adverse conditions: Insights from behavioral, computational, and neuroscience research

Adult speech perception reflects the long-term regularities of the native language, but it is also flexible such that it accommodates and adapts to adverse listening conditions and short-term deviations from native-language norms. The purpose of this article is to examine how the broader neuroscience literature can inform and advance research efforts in understanding the neural basis of flexibility and adaptive plasticity in speech perception. Specifically, we highlight the potential role of learning algorithms that rely on prediction error signals and discuss specific neural structures that are likely to contribute to such learning. To this end, we review behavioral studies, computational accounts, and neuroimaging findings related to adaptive plasticity in speech perception. Already, a few studies have alluded to a potential role of these mechanisms in adaptive plasticity in speech perception. Furthermore, we consider research topics in neuroscience that offer insight into how perception can be adaptively tuned to short-term deviations while balancing the need to maintain stability in the perception of learned long-term regularities. Consideration of the application and limitations of these algorithms in characterizing flexible speech perception under adverse conditions promises to inform theoretical models of speech. © 2014 Guediche, Blumstein, Fiez and Holt

Do Multi-Sense Embeddings Improve Natural Language Understanding?

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while `multi-sense' methods have been proposed and tested on artificial word-similarity tasks, we don't know if they improve real natural language understanding tasks. In this paper we introduce a multi-sense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language understanding. We then test the performance of our model on part-of-speech tagging, named entity recognition, sentiment analysis, semantic relation identification and semantic relatedness, controlling for embedding dimensionality. We find that multi-sense embeddings do improve performance on some tasks (part-of-speech tagging, semantic relation identification, semantic relatedness) but not on others (named entity recognition, various forms of sentiment analysis). We discuss how these differences may be caused by the different role of word sense information in each of the tasks. The results highlight the importance of testing embedding models in real applications