Derivational morphology in the German mental lexicon: A dual mechanism account

The Dual Mechanism Model posits two different cognitive mechanisms for morphologically complex word forms: decomposition of regulars into stems and exponents, and full-form storage for irregulars. Most of the research in this framework has focused on contrasts between productive and non-productive inflection. In this paper, we extend the model to derivational morphology. Our studies indicate that productive derivation shows affinities with both productive and non-productive inflection. We argue that these results support the linguistic distinction between derivation and inflection, particularly as it is represented in realization-based models of morphology

Listeners normalize speech for contextual speech rate even without an explicit recognition task

Speech can be produced at different rates. Listeners take this rate variation into account by normalizing vowel duration for contextual speech rate: An ambiguous Dutch word /m?t/ is perceived as short /mAt/ when embedded in a slow context, but long /ma:t/ in a fast context. Whilst some have argued that this rate normalization involves low-level automatic perceptual processing, there is also evidence that it arises at higher-level cognitive processing stages, such as decision making. Prior research on rate-dependent speech perception has only used explicit recognition tasks to investigate the phenomenon, involving both perceptual processing and decision making. This study tested whether speech rate normalization can be observed without explicit decision making, using a cross-modal repetition priming paradigm. Results show that a fast precursor sentence makes an embedded ambiguous prime (/m?t/) sound (implicitly) more /a:/-like, facilitating lexical access to the long target word "maat" in a (explicit) lexical decision task. This result suggests that rate normalization is automatic, taking place even in the absence of an explicit recognition task. Thus, rate normalization is placed within the realm of everyday spoken conversation, where explicit categorization of ambiguous sounds is rare

Learning semantic sentence representations from visually grounded language without lexical knowledge

Current approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state-of-the-art on two popular image-caption retrieval benchmark data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Phonological (un)certainty weights lexical activation

Spoken word recognition involves at least two basic computations. First is matching acoustic input to phonological categories (e.g. /b/, /p/, /d/). Second is activating words consistent with those phonological categories. Here we test the hypothesis that the listener's probability distribution over lexical items is weighted by the outcome of both computations: uncertainty about phonological discretisation and the frequency of the selected word(s). To test this, we record neural responses in auditory cortex using magnetoencephalography, and model this activity as a function of the size and relative activation of lexical candidates. Our findings indicate that towards the beginning of a word, the processing system indeed weights lexical candidates by both phonological certainty and lexical frequency; however, later into the word, activation is weighted by frequency alone.Comment: 6 pages, 4 figures, accepted at: Cognitive Modeling and Computational Linguistics (CMCL) 201

Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English

This paper reports results from masked priming experiments investigating regular past-tense forms and deadjectival nominalizations with -ness and -ity in adult native (L1) speakers of English and in different groups of advanced adult second language (L2) learners of English. While the L1 group showed efficient priming for both inflected and derived word forms, the L2 learners demonstrated repetition-priming effects (like the L1 group), but no priming for inflected and reduced priming for derived word forms. We argue that this striking contrast between L1 and L2 processing supports the view that adult L2 learners rely more on lexical storage and less on combinatorial processing of morphologically complex words than native speakers.</jats:p

Cross-linguistic activation in bilingual sentence processing: the role of word class meaning

This study investigates how categorial (word class) semantics influences cross-linguistic interactions when reading in L2. Previous homograph studies paid little attention to the possible influence of different word classes in the stimulus material on cross-linguistic activation. The present study examines the word recognition performance of Dutch-English bilinguals who performed a lexical decision task to word targets appearing in a sentence. To determine the influence of word class meaning, the critical words either showed a word class overlap (e. g. the homograph tree [ noun], which means "step" in Dutch) or not (e.g. big [ADJ], which is a noun in Dutch meaning "piglet"). In the condition of word class overlap, a facilitation effect was observed, suggesting that both languages were active. When there was no word class overlap, the facilitation effect disappeared. This result suggests that categorial meaning affects the word recognition process of bilinguals