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Timothy Baldwin - One of the best experts on this subject based on the ideXlab platform.
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best topic Word Selection for topic labelling
International Conference on Computational Linguistics, 2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
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COLING (Posters) - Best Topic Word Selection for Topic Labelling
2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
Jey Han Lau - One of the best experts on this subject based on the ideXlab platform.
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best topic Word Selection for topic labelling
International Conference on Computational Linguistics, 2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
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COLING (Posters) - Best Topic Word Selection for Topic Labelling
2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
F. -xavier Alario - One of the best experts on this subject based on the ideXlab platform.
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Challenges to developing time-based signal detection models for Word production.
Cognitive neuropsychology, 2019Co-Authors: Royce Anders, F. -xavier AlarioAbstract:A crucial process underlying language production is Word Selection (Levelt, 1989, Levelt, Roelofs, & Meyer, 1999). In this process, the semantic components of an intended idea must link to the lexi...
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Probing the link between cognitive control and lexical Selection in monolingual speakers
Annee Psychologique, 2012Co-Authors: F. -xavier Alario, Johannes C. Ziegler, Stéphanie Massol, Bruno CaraAbstract:Whether bilinguals perform better than monolinguals in non-linguistic cognitive control tasks is a matter of current debate. Bilinguals are constantly required to switch and discriminate between their two languages. Such additional discrimination requirements are thought to result in improved domain-general cognitive control abilities compared to monolinguals. This rationale was examined by taking it one step further. A general link between general response Selection (i.e., cognitive control mechanisms) and Word Selection should be apparent within the monolingual population, thus the natural variability present in response discrimination abilities should predict Word Selection in a population of monolinguals. A large group of young monolingual pupils were tested in the non-linguistic Simon task and in a picture naming Word Selection task. Selection difficulty was manipulated in both tasks. There were clear effects of response Selection difficulty in either task, but there was no relationship between them at the individual level. This null effect provides no support for the hypothesis tested. It prompts a tentative discussion of exactly what process, in bilingual language use, may be capable of promoting cognitive control abilities.
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The role of visual form in lexical access: Evidence from Chinese classifier production
Cognition, 2010Co-Authors: Jingyi Geng, F. -xavier AlarioAbstract:The interface between the conceptual and lexical systems was investigated in a Word production setting. We tested the effects of two conceptual dimensions - semantic category and visual shape - on the Selection of Chinese nouns and classifiers. Participants named pictures with nouns ("rope") or classifier-noun phrases ("one-classifier-rope") in three blocked picture naming experiments. In Experiment 1, we observed larger semantic category interference with phrases than with nouns, suggesting comparable semantic categorical effects on classifier and noun Selection. In Experiments 2 and 3, items with similar shapes produced an interference effect when they were named with classifier-noun phrases, but not with bare nouns. This indicates that object shape modulates classifier (but not noun) Selection. We conclude that object shape properties can by themselves influence Word Selection processes just as semantic relationships (captured by semantic category) do. The factors operating during Word Selection may be more diverse than has been previously thought.
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Grammatical and nongrammatical contributions to closed-class Word Selection
Journal of Experimental Psychology: Learning Memory and Cognition, 2008Co-Authors: F. -xavier Alario, Pauline Ayora, Albert Costa, Alissa MelingerAbstract:Closed-class Word Selection was investigated by focusing on determiner production. Native speakers from three different languages named pictures of objects using determiner plus noun phrases (e.g., in French ``la table'' [the(feminine) table], while ignoring distractor determiners printed on the pictures (e.g., ``le'' [the(masculine)]. The target and distractor expressed either shared or different grammatical and nongrammatical features (gender, number, and definiteness). A gender-facilitation effect was observed and attributed to noun processing. Crucially, across five experiments, distractors that shared a feature with the target determiner never resulted in longer naming latencies than distractors that were more different. These results indicate that activating related candidates is not detrimental for determiner retrieval, suggesting a noncompetitive mechanism of closed-class Word Selection.
David Newman - One of the best experts on this subject based on the ideXlab platform.
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best topic Word Selection for topic labelling
International Conference on Computational Linguistics, 2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
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COLING (Posters) - Best Topic Word Selection for Topic Labelling
2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
Sarvnaz Karimi - One of the best experts on this subject based on the ideXlab platform.
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best topic Word Selection for topic labelling
International Conference on Computational Linguistics, 2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
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COLING (Posters) - Best Topic Word Selection for Topic Labelling
2010Co-Authors: Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy BaldwinAbstract:This paper presents the novel task of best topic Word Selection, that is the Selection of the topic Word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic Word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic Word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
