Publications

Abstract

A new psycholinguistically motivated and neural network based model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons which pass on sensor potentials to a word neuron. In experiments with a small lexicon which includes groups of very similar word forms, the model meets high standards with respect to word recognition and simulates a number of wellknown psycholinguistical effects.

Abstract

The ISOcat Data Category Registry provides a community computing environment for creating, storing, retrieving, harmonizing and standardizing data category specifications (DCs), used to register linguistic terms used in various fields. This chapter recounts the history of DC documentation in TC 37, beginning from paper-based lists created for lexicographers and terminologists and progressing to the development of a web-based resource for a much broader range of users. While describing the considerable strides that have been made to collect a very large comprehensive collection of DCs, it also outlines difficulties that have arisen in developing a fully operative web-based computing environment for achieving consensus on data category names, definitions, and selections and describes efforts to overcome some of the present shortcomings and to establish positive working procedures designed to engage a wide range of people involved in the creation of language resources.

Abstract

Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels. Although we can do such unitization/segmentation easily, its cognitive mechanism is still not clear. This paper proposes an unsupervised model, Less-is-Better (LiB), to simulate the human cognitive process with respect to language unitization/segmentation. LiB follows the principle of least effort and aims to build a lexicon which minimizes the number of unit tokens (alleviating the effort of analysis) and number of unit types (alleviating the effort of storage) at the same time on any given corpus. LiB’s workflow is inspired by empirical cognitive phenomena. The design makes the mechanism of LiB cognitively plausible and the computational requirement light-weight. The lexicon generated by LiB performs the best among different types of lexicons (e.g. ground-truth words) both from an information-theoretical view and a cognitive view, which suggests that the LiB lexicon may be a plausible proxy of the mental lexicon.

Abstract

To acquire the meaning of a verb, language learners not only need to find the correct mapping between a specific verb and an action or event in the world, but also infer the underlying relational meaning that the verb encodes. Most verb naming instances in naturalistic contexts are highly ambiguous as many possible actions can be embedded in the same scenario and many possible verbs can be used to describe those actions. To understand whether learners can find the correct verb meaning from referentially ambiguous learning situations, we conducted three experiments using the Human Simulation Paradigm with adult learners. Our results suggest that although finding the right verb meaning from one learning instance is hard, there is a statistical solution to this problem. When provided with multiple verb learning instances all referring to the same verb, learners are able to aggregate information across situations and gradually converge to the correct semantic space. Even in cases where they may not guess the exact target verb, they can still discover the right meaning by guessing a similar verb that is semantically close to the ground truth.

Abstract

In the word-learning domain, both adults and young children are able to find the correct referent of a word from highly ambiguous contexts that involve many words and objects by computing distributional statistics across the co-occurrences of words and referents at multiple naming moments (Yu & Smith, 2007; Smith & Yu, 2008). However, there is still debate regarding how learners accumulate distributional information to learn object labels in natural learning environments, and what underlying learning mechanism learners are most likely to adopt. Using the Human Simulation Paradigm (Gillette, Gleitman, Gleitman & Lederer, 1999), we found that participants’ learning performance gradually improved and that their ability to remember and carry over partial knowledge from past learning instances facilitated subsequent learning. These results support the statistical learning model that word learning is a continuous process.

Abstract

In informal interaction, speakers rarely thank a person who has complied with a request. Examining data from British English, German, Italian, Polish, and Telugu, we ask when speakers do thank after compliance. The results show that thanking treats the other’s assistance as going beyond what could be taken for granted in the circumstances. Coupled with the rareness of thanking after requests, this suggests that cooperation is to a great extent governed by expectations of helpfulness, which can be long-standing, or built over the course of a particular interaction. The higher frequency of thanking in some languages (such as English or Italian) suggests that cultures differ in the importance they place on recognizing the other’s agency in doing as requested.

Abstract

This paper reports on an exploration of the ways in which multiple entities are expressed in Turkish Sign Language (TİD). The (descriptive and quantitative) analyses provided are based on a corpus of both spontaneous data and specifically elicited data, in order to provide as comprehensive an account as possible. We have found several devices in TİD for expression of multiple entities, in particular localization, spatial plural predicate inflection, and a specific form used to express multiple entities that are side by side in the same configuration (not reported for any other sign language to date), as well as numerals and quantifiers. In contrast to some other signed languages, TİD does not appear to have a productive system of plural reduplication. We argue that none of the devices encountered in the TİD data is a genuine plural marking device and that the plural interpretation of multiple entity localizations and plural predicate inflections is a by-product of the use of space to indicate the existence or the involvement in an event of multiple entities.