Automated Program Description

The Programmer's apprentice (PA) is an automated program development tool. The PA depends upon a library of common algorithms (cliches) as the source of its knowledge about programming. The PA uses these cliches to understand how a program is implemented. This knowledge may also be used to explain to a user of the PA how the program is implemented. The problem with any explanation or description is knowing how much information to present, and how much information to hide. A set of simple heuristics for doing this can be used with the cliche representation of a program to produce reasonable descriptions of parts of programs. The system described combines "canned" phrases corresponding to cliche parts to form explanations. The process is fast and appears to be easily extensible to future versions of the PA and other domains.MIT Artificial Intelligence Laborator

Programming Cliches and Cliche Extraction

This paper is a revision of an earlier Bachelor's thesis.The programmer's apprentice (PA) is an automated program development tool. The PA depends upon a library of common algorithms (cliches) as the source of its knowledge about programming. The PA can be made more usable if programmers not familiar with its implementation can add programming knowledge to the PA's library. This paper describes cliches and a technique for adding them to the library. Because cliches often do not correspond to complete code, the library can not simply be a collection of programs. Instead, a plan representation is used. The approach taken for adding knowledge to the library is one of cliche extraction. A program containing a particular cliche is converted to its plan. The plan is pruned, with the results of the pruned plan being displayed in a code-like form. Eventually, only the cliche remains. The cliche is then added to the library.MIT Artificial Intelligence Laborator

Field crossings : hybridizing the urban park

Thesis (M.Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 2003.Includes bibliographical references (p. 102-103).The growth and identity of urban open space is a vital issue facing our cities today. The development and revitalization of old industrial centers in the United States has prompted urban transformations in usage, densification and demographics. These shifting neighborhoods call for a reconsideration of the makeup and syntax of their associated green spaces. The design of this urban landscape is not currently positioned to take advantage of limited spatial opportunities while meeting increasingly diverse programmatic needs. Traditional park typologies must respond to contemporary forces, varying leisure practices and allow for new interactions with an evolving city. This thesis posits a new model for parks and their architecture within changing urban neighborhoods. It explores how parks can accommodate these transformations through the topics of imbedded infrastructure, flexibility, prototyping and merging public and private usage. It seeks to create more humane and vital open spaces by adding functional and diversified occupations that respond to specific contextual requirements. This thesis looks to understand how both the landscape and its built architecture can work together to become a more viable model for the next century.Scott Marshall Cyphers.M.Arch

Query understanding enhanced by hierarchical parsing structures

Query understanding has been well studied in the areas of information retrieval and spoken language understanding (SLU). There are generally three layers of query understanding: domain classification, user intent detection, and semantic tagging. Classifiers can be applied to domain and intent detection in real systems, and semantic tagging (or slot filling) is commonly defined as a sequence-labeling task-- mapping a sequence of words to a sequence of labels. Various statistical features (e.g., n-grams) can be extracted from annotated queries for learning label prediction models; however, linguistic characteristics of queries, such as hierarchical structures and semantic relationships, are usually neglected in the feature extraction process. In this work, we propose an approach that leverages linguistic knowledge encoded in hierarchical parse trees for query understanding. Specifically, for natural language queries, we extract a set of syntactic structural features and semantic dependency features from query parse trees to enhance inference model learning. Experiments on real natural language queries show that augmenting sequence labeling models with linguistic knowledge can improve query understanding performance in various domains. Index Terms — query understanding, semantic tagging, linguistic parsin

The Development of Speech Research Tools on MIT\u27s Lisp Machine-based Workstations

In recent years, a number of useful speech- and language-related research tools have been under development at MIT. These tools are aids for efficiently analyzing the acoustic characteristics of speech and the phonological properties of a language. They are playing a valuable role in our own research, as well as in research conducted elsewhere. This paper describes several of the systems being developed for use on our Lisp Machine workstations

A Vector Space Approach for Aspect Based Sentiment Analysis

Abstract Vector representations for language has been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Aspect Based Sentiment Analysis. In particular, we target three sub-tasks namely aspect term extraction, aspect category detection, and aspect sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. We utilize vector representations to compute various vectorbased features and conduct extensive experiments to demonstrate their effectiveness. Using simple vector based features, we achieve F1 scores of 79.91% for aspect term extraction, 86.75% for category detection, and the accuracy 72.39% for aspect sentiment prediction

ASGARD: A PORTABLE ARCHITECTURE FOR MULTILINGUAL DIALOGUE SYSTEMS

Spoken dialogue systems have been studied for years, yet portability is still one of the biggest challenges in terms of language extensibility, domain scalability, and platform compatibility. In this work, we investigate the portability issue from the language understanding perspective and present the Asgard architecture, a CRF-based (Conditional Random Fields) and crowd-sourcing-centered framework, which supports expert-free development of multilingual dialogue systems and seamless deployment to mobile platforms. Combinations of linguistic and statistical features are employed for multilingual semantic understanding, such as n-grams, tokenization and part-of-speech. English and Mandarin systems in various domains (movie, flight and restaurant) are implemented with the proposed framework and ported to mobile platforms as well, which sheds lights on large-scale speech App development. Index Terms — Spoken dialogue systems, multilingual, portabilit

Spoken command of large mobile robots in outdoor environments

We describe a speech system for commanding robots in human-occupied outdoor military supply depots. To operate in such environments, the robots must be as easy to interact with as are humans, i.e. they must reliably understand ordinary spoken instructions, such as orders to move supplies, as well as commands and warnings, spoken or shouted from distances of tens of meters. These design goals preclude close-talking microphones and “push-to-talk” buttons that are typically used to isolate commands from the sounds of vehicles, machinery and non-relevant speech. We used multiple microphones to provide omnidirectional coverage. A novel voice activity detector was developed to detect speech and select the appropriate microphone to listen to. Finally, we developed a recognizer model that could successfully recognize commands when heard amidst other speech within a noisy environment. When evaluated on speech data in the field, this system performed significantly better than a more computationally intensive baseline system, reducing the effective false alarm rate by a factor of 40, while maintaining the same level of precision