Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming comp...

Translating in technical domains is a wellknown problem in SMT, as the lack of parallel documents causes significant problems of sparsity. We discuss and compare different strategies for enriching SMT systems built on general domain data with bilingual terminology mined from comparable corpora. In particular, we focus on the targetlanguage inflection of the terminology data and present a pipeli...

Students often face process losses when learning together via text-based online environments. Computer-supported collaboration scripts can scaffold collaborative learning processes by distributing roles and activities and thus facilitate acquisition of domain-specific as well as domain-general knowledge, such as knowledge on argumentation. Possibly, individual learners would require less additi...

Part of speech tagging is a fundamental component in many NLP systems. When taggers developed in one domain are used in another domain, the performance can degrade considerably. We present a method for developing taggers for new domains without requiring POS annotated text in the new domain. Our method involves using raw domain text and identifying related words to form a domain specific lexico...

Current statistical parsers tend to perform well only on their training domain and nearby genres. While strong performance on a few related domains is sufficient for many situations, it is advantageous for parsers to be able to generalize to a wide variety of domains. When parsing document collections involving heterogeneous domains (e.g. the web), the optimal parsing model for each document is...

Students often face process losses when learning together via text-based online environments. Computer-supported collaboration scripts can scaffold collaborative learning processes by distributing roles and activities and thus facilitate acquisition of domain-specific as well as domain-general knowledge, such as knowledge on argumentation. Possibly, individual learners would require less additi...

Automatically identifying implicit discourse relations using annotated data and raw corpora This paper presents a system for identifying « implicit » discourse relations (that is, relations that are not marked by a discourse connective). Given the little amount of available annotated data for this task, our system also resorts to additional automatically labeled data wherein unambiguous connect...

In this paper we improve over the hierarchical Pitman-Yor processes language model in a cross-domain setting by adding skipgrams as features. We find that adding skipgram features reduces the perplexity. This reduction is substantial when models are trained on a generic corpus and tested on domain-specific corpora. We also find that within-domain testing and crossdomain testing require differen...

Although it is known that single-domain proteins fold and unfold by parallel pathways, demonstration of this expectation has been difficult to establish in experiments. Unfolding rate, [Formula: see text], as a function of force f, obtained in single-molecule pulling experiments on src SH3 domain, exhibits upward curvature on a [Formula: see text] plot. Similar observations were reported for ot...

Text Mining is the process of extraction of knowledge from large amounts of text. Information retrieval gains more focus due to tremendous increase of textual information in web servers. It is a very challenging task to process, organize, analyze and extract knowledge from huge volumes of unstructured text. Traditional text classification algorithms needs well defined text corpus to train and c...