Domain adaptation with minimal training

The performance of a machine learning model trained on labeled data of a (source) domain degrades severely when they are tested on a different (target) domain. Traditional approaches deal with this problem by training a new model for every target domain. In natural language processing, top performing systems often use multiple interconnected models; therefore training all of them for every target domain is computationally expensive. Moreover, retraining the model for the target domain requires access to the labeled data from the source domain which may not be available to end users due to copyright issues. This thesis is a study on how to adapt to a target domain, using the system trained on source domain and avoiding the cost of retraining and the need for access to the source labeled data. This thesis identifies two key ingredients for adaptation without training: broad coverage resources and constraints. We show how resources like Wikipedia, VerbNet and WordNet that contain comprehensive coverage of entities, semantic roles and words in English can help a model adapt to the target domain. For the task of semantic role labeling, we show that in the decision phase, we can replace a linguistic unit (e.g. verb, word) with another equivalent linguistic unit residing in the same cluster defined in these resources (e.g. VerbNet, WordNet) such that after replacement, text becomes more like text on which the model was trained. We show that the model's output is more accurate on the transformed text than on original text. In another instance, we show how to use a system for linking mentions to Wikipedia concepts for adaptation of a named entity recognition system. Since Wikipedia has a broad domain coverage, the linking system is robust across domain variations. Therefore, jointly performing entity recognition and linking improves the accuracy of entity recognition on the target domain without requiring training of a new system for the new domain. In all cases, we show how to use intuitive constraints to guide the model into making coherent predictions. We show how incorporating prior knowledge about a new domain as declarative constraints into the decision phase can improve performance of a model on the new domain. When such prior knowledge is unavailable, we show how to acquire knowledge automatically from unlabeled text from the new domain and domains similar to both source and target domains

Learning Human Factors/Ergonomics (HFE) in Architectural Education: A Study of Studio Approach in Bangladesh

All the human activities take place in the built environment and therefore human factors/ergonomics (HFE) is an essential design consideration for the built environment designing process. Surprisingly, there have been limited studies on integrating HFE in the design process as well as in the education of architecture. Teaching HFE in architecture is different from teaching HFE in the disciplines that focuses on precise ergonomic application. Architectural education primarily deals with accommodating human activities in the built environment; and therefore, teaching HFE focuses on anthropometry, space standards, and an in-depth understanding of space requirements for relevant human activities. In architectural education, HFE can be taught as theory courses and/or in the design studio courses. This article focuses on the studio approach with an overview of several studio courses and a meticulous study of a studio course that teaches HFE principles. The study follows desktop research, participant observation, and a questionnaire survey. It is observed that the studio approach provides an opportunity for a deeper understanding of the HFE principles and their application in space design. Specifically, the practice of learning within the studio setup, group work and peer critique, assessment and feedback with critique sessions before the evaluation, etc. have a profound impact on the students to internalizeHFE in their thought process. A survey among the students also indicates the effectiveness of the studio approach for learning HFE.

A Wikipedia based Algorithm for Online Adaptation of a Syntactic Parser

Adaptation of models to a new domain is important for many natural language tasks. Because without adaptation, NLP tools trained on news domain achieve sub par results on other domains. In many practical scenarios, the identity of the domain of the test set is unknown. For this difficult but important setting, we propose a novel method of adaptation using entity disambiguation systems to Wikipedia. We get significant improvements for adapting a syntactic parser trained on news domain to biomedical domain.unpublishe

Teaching with Examples in A Real Environment

Teaching is challenging in a real environment. One problem is that not all examples may be available to teach. We show how to teach several important concept classes namely conjunction, disjunction and linear threshold functions under different characterizations of the domain of available examples. We show that a monotone linear threshold function is teachable using a polynomial number of examples when the accessible domain is defined by the intersection of multiple monotone linear threshold functions. Also, a teacher may not be smart enough to know the target concept exactly but he may be able to provide better examples from available examples. We show how to teach without knowing the target concept exactly and using only available examples. Our experiments on the benchmark data sets of text categorization and movie review classification show that the algorithm Partial Instance Feedback (PIF) results in 8-11% error reduction over active learning and 16-18% error reduction over random sampling.unpublishednot peer reviewe

Electrochemical Vicinal C–H Difunctionalization of Saturated Azaheterocycles

A method to functionalize two vicinal C–H bonds of saturated azaheterocycles is described. The procedure involves subjecting the substrate to a mixture of hydrochloric acid, acetic acid, and acetic anhydride in an undivided electrochemical cell at a constant current, resulting in stereoselective conversion to the corresponding α-acetoxy-β-chloro derivative. The α-position can be readily substituted with a range of other groups, including alkyl, aryl, allyl, alkynyl, alkoxy, or azido functionalities. Furthermore, we demonstrate that the β-chloro position can be engaged in Suzuki cross-coupling. This protocol thus enables the rapid diversification of simple five-, six-, and seven-membered saturated azaheterocycles at two adjacent positions