A multimodal learning interface for grounding spoken language in sensory perceptions
Most speech interfaces are based on natural language processing techniques that use pre-defined symbolic representations of word meanings and process only linguistic information. To understand and use language like their human counterparts in multimodal human-computer interaction, computers need to acquire spoken language and map it to other sensory perceptions. This paper presents a multimodal interface that learns to associate spoken language with perceptual features by being situated in users' everyday environments and sharing user-centric multisensory information. The learning interface is trained in unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. We collect acoustic signals in concert with multisensory information from non-speech modalities, such as user's perspective video, gaze positions, head directions and hand movements. The system firstly estimates users' focus of attention from eye and head cues. Attention, as represented by gaze fixation, is used for spotting the target object of user interest. Attention switches are calculated and used to segment an action sequence into action units which are then categorized by mixture hidden Markov models. A multimodal learning algorithm is developed to spot words from continuous speech and then associate them with perceptually grounded meanings extracted from visual perception and action. Successful learning has been demonstrated in the experiments of three natural tasks: "unscrewing a jar", "stapling a letter" and "pouring water".

title: Attentional object spotting by integrating multimodal input
abstract: An intelligent human-computer interface is expected to allow computers to work with users in a cooperative manner. To achieve this goal, computers need to be aware of user attention and provide assistance without explicit user requests. Cognitive studies of eye movements suggest that in accomplishing well-learned tasks, the performer's focus of attention is locked onto ongoing work and more than 90% of eye movements are closely related to the objects being manipulated in the tasks. In light of this, we have developed an attentional object spotting system that integrates multimodal data consisting of eye position, head position and video from the "first-person" perspective. To detect the user's focus of attention, we modeled eye gaze and head movements using a hidden Markov model (HMM) representation. For each attentional point in time, the object of user interest is automatically extracted and recognized. We report the results of experiments on finding attentional objects in the natural task of "making a peanut-butter sandwich".

title: A multimodal learning interface for grounding spoken language in sensory perceptions
abstract: We present a multimodal interface that learns words from natural interactions with users. In light of studies of human language development, the learning system is trained in an unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. The system collects acoustic signals in concert with user-centric multisensory information from nonspeech modalities, such as user's perspective video, gaze positions, head directions, and hand movements. A multimodal learning algorithm uses this data to first spot words from continuous speech and then associate action verbs and object names with their perceptually grounded meanings. The central ideas are to make use of nonspeech contextual information to facilitate word spotting, and utilize body movements as deictic references to associate temporally cooccurring data from different modalities and build hypothesized lexical items. From those items, an EM-based method is developed to select correct word--meaning pairs. Successful learning is demonstrated in the experiments of three natural tasks: "unscrewing a jar," "stapling a letter," and "pouring water."

title: The emergence of links between lexical acquisition and object categorization: a computational study
abstract: Language is about symbols, and those symbols must be grounded in the physical world. Children learn to associate language with sensorimotor experiences during their development. In light of this, we first provide a computational account of how words are mapped to their perceptually grounded meanings. Moreover, the main part of this work proposes and implements a computational model of how word learning influences the formation of object categories to which those words refer. This model simulates the bi-directional relationship between word and object category learning: (1) object categorization provides mental representations of meanings that are mapped to words to form lexical items; (2) linguistic labels help object categorization by providing additional teaching signals; and (3) these two learning processes interplay with each other and form a developmental feedback loop. Compared with the method that performs these two tasks separately, our model shows promising improvements in both word-to-world mapping and perceptual categorization, suggesting a unified view of lexical and category learning in an integrative framework. Most importantly, this work provides a cognitively plausible explanation of the mechanistic nature of early word learning and object learning from co-occurring multisensory data.

title: On the Integration of Grounding Language and Learning Objects
abstract: This paper presents a multimodal learning system that can ground spoken names of objects in their physical referents and learn to recognize those objects simultaneously from naturally co-occurring multisensory input. There are two technical problems involved: (1) the correspondence problem in symbol grounding - how to associate words (symbols) with their perceptually grounded meanings from multiple cooccurrences between words and objects in the physical environment. (2) object learning - how to recognize and categorize visual objects. We argue that those two problems can be fundamentally simplified by considering them in a general system and incorporating the spatio-temporal and cross-modal constraints of multimodal data. The system collects egocentric data including image sequences as well as speech while users perform natural tasks. It is able to automatically infer the meanings of object names from vision, and categorize objects based on teaching signals potentially encoded in speech. The experimental results reported in this paper reveal the effectiveness of using multimodal data and integrating heterogeneous techniques in machine learning, natural language processing and computer vision.

title: The Role of Embodied Intention in Early Lexical Acquisition
abstract: We examine the influence of inferring interlocutors' referential intentions from their body movements at the early stage of lexical acquisition. By testing human participants and comparing their performances in different learning conditions, we find that those embodied intentions facilitate both word discovery and word-meaning association. In light of empirical findings, the main part of this article presents a computational model that can identify the sound patterns of individual words from continuous speech, using nonlinguistic contextual information, and employ body movements as deictic references to discover word-meaning associations. To our knowledge, this work is the first model of word learning that not only learns lexical items from raw multisensory signals to closely resemble infant language development from natural environments, but also explores the computational role of social cognitive skills in lexical acquisition.

title: TIMBER: A native XML database
abstract: This paper describes the overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan. The system is based upon a bulk algebra for manipulating trees, and natively stores XML. New access methods have been developed to evaluate queries in the XML context, and new cost estimation and query optimization techniques have also been developed. We present performance numbers to support some of our design decisions. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.

title: A Methodology to Retrieve Text Documents from Multiple Databases
abstract: This paper presents a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, the contents of databases are indicated approximately by database representatives. Databases are ranked using their representatives with respect to the given query. We provide a necessary and sufficient condition to rank the databases optimally. In order to satisfy this condition, we provide three estimation methods. One estimation method is intended for short queries; the other two are for all queries. Second, we provide an algorithm, OptDocRetrv, to retrieve documents from the databases according to their rank and in a particular way. We show that if the databases containing the n most similar documents for a given query are ranked ahead of other databases, our methodology will guarantee the retrieval of the n most similar documents for the query. When the number of databases is large, we propose to organize database representatives into a hierarchy and employ a best-search algorithm to search the hierarchy. It is shown that the effectiveness of the best-search algorithm is the same as that of evaluating the user query against all database representatives.

title: Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together
abstract: Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: .

title: Fully automatic wrapper generation for search engines
abstract: When a query is submitted to a search engine, the search engine returns a dynamically generated result page containing the result records, each of which usually consists of a link to and/or snippet of a retrieved Web page. In addition, such a result page often also contains information irrelevant to the query, such as information related to the hosting site of the search engine and advertisements. In this paper, we present a technique for automatically producing wrappers that can be used to extract search result records from dynamically generated result pages returned by search engines. Automatic search result record extraction is very important for many applications that need to interact with search engines such as automatic construction and maintenance of metasearch engines and deep Web crawling. The novel aspect of the proposed technique is that it utilizes both the visual content features on the result page as displayed on a browser and the HTML tag structures of the HTML source file of the result page. Experimental results indicate that this technique can achieve very high extraction accuracy.

title: Personalized Web search for improving retrieval effectiveness
abstract: Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.