Articles

We demonstrate the value of using con- text in a new-information detection sys- tem that achieved the highest precision scores at the Text Retrieval Conference’s Novelty Track in 2004. In order to de- termine whether information within a sen- tence has been seen in material read pre- viously, our system …...

We present two methods for incorporat- ing detailed features in a Spanish parser, building on a baseline model that is a lex- icalized PCFG. The first method exploits Spanish morphology, and achieves an F1 constituency score of 83.6%. This is an improvement over 81.2% accuracy for the baseline, which makes …...

Many learning tasks have subtasks for which much training data exists. Therefore, we want to transfer learning from the old, general- purpose subtask to a more specific new task, for which there is often less data. While work in transfer learning often considers how the old task should affect learning …...

We should not have to look at the en- tire corpus (e.g., the Web) to know if two words are associated or not.1 A powerful sampling technique called Sketches was originally introduced to remove duplicate Web pages. We generalize sketches to estimate contingency tables and associa- tions, using a maximum …...

In this paper, we look at comparing high- accuracy context-free parsers with high- accuracy finite-state (shallow) parsers on several shallow parsing tasks. We show that previously reported compar- isons greatly under-estimated the perfor- mance of context-free parsers for these tasks. We also demonstrate that context- free parsers can train effectively …...

We consider the problem of training logis- tic regression models for binary classifi- cation in information extraction and infor- mation retrieval tasks. Fitting probabilis- tic models for use with such tasks should take into account the demands of the task- specific utility function, in this case the well-known F-measure, which …...

In this work we propose a transla- tion model for monolingual sentence retrieval. We propose four methods for constructing a parallel corpus. Of the four methods proposed, a lexi- con learned from a bilingual Arabic- English corpus aligned at the sentence level performs best, significantly im- proving results over the …...

In statistical machine translation, estimat- ing word-to-word alignment probabilities for the translation model can be difficult due to the problem of sparse data: most words in a given corpus occur at most a handful of times. With a highly inflected language such as Czech, this problem can be particularly severe. …...

We analyze models for semantic role assignment by defining a meta-model that abstracts over features and learning paradigms. This meta-model is based on the concept of role confusability, is de- fined in information-theoretic terms, and predicts that roles realized by less specific grammatical functions are more difficult to assign. We …...

In this paper, we study the impact of a group of features extracted automatically from machine-generated parse trees on coreference resolution. One focus is on designing syn- tactic features using the binding theory as the guideline to improve pronoun resolution, al- though linguistic phenomenon such as apposi- tion is also …...

This paper reports a preliminary study addressing two challenges in measuring the effectiveness of information extrac- • tion (IE) technology: Developing a methodology for ex- trinsic evaluation of IE; and, • Estimating the impact of improving IE technology on the ability to per- form an application task. The methodology described …...

This paper focuses on semantic role la- beling using automatically-generated syn- tactic information. A simple and robust strategy for system combination is pre- sented, which allows to partially recover from input parsing errors and to signif- icantly boost results of individual sys- tems. This combination scheme is also very flexible …...

Accurate lemmatization of German nouns mandates the use of a lexicon. Compre- hensive lexicons, however, are expensive to build and maintain. We present a self- learning lemmatizer capable of automati- cally creating a full-form lexicon by pro- cessing German documents. 1...

We use logical inference techniques for recognising textual entailment. As the performance of theorem proving turns out to be highly dependent on not read- ily available background knowledge, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. …...

In this paper, we extend an existing parser to produce richer output annotated with function labels. We obtain state-of-the-art results both in function labelling and in parsing, by automatically relabelling the Penn Treebank trees. In particular, we ob- tain the best published results on seman- tic function labels. This suggests …...

We present Evita, an application for rec- ognizing events in natural language texts. Although developed as part of a suite of tools aimed at providing question answer- ing systems with information about both temporal and intensional relations among events, it can be used independently as an event extraction tool. It …...

This paper uses human verb associations as the basis for an investigation of verb properties, focusing on semantic verb re- lations and prominent nominal features. First, the lexical semantic taxonymy Ger- maNet is checked on the types of clas- sic semantic relations in our data; verb- verb pairs not covered …...

A reading comprehension (RC) system attempts to understand a document and returns an answer sentence when posed with a question. RC resembles the ad hoc question answering (QA) task that aims to extract an answer from a collection of documents when posed with a question. However, since RC focuses only …...

This paper studies how to go beyond relevance and enable a filtering system to learn more in- teresting and detailed data driven user models from multiple forms of evidence. We carry out a user study using a real time web based per- sonal news filtering system, and collect exten- sive …...

In addition to information, text con- tains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction prob- lem empirically, using supervised machine learning with the SNoW learning archi- tecture. The goal is to classify the emo- tional affinity of sentences in the narra- tive domain of …...