Articles

The title of a document has two roles, to give a compact summary and to lead the reader to read the document. Conventional title gener- ation focuses on finding key expressions from the author’s wording in the document to give a compact summary and pays little attention to the reader’s …...

We evaluate probabilistic models of verb argument structure trained on a corpus of verbs and their syn- tactic arguments. Models designed to represent pat- terns of verb alternation behavior are compared with generic clustering models in terms of the perplexity assigned to held-out test data. While the special- ized models …...

While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications such as Question Answering and Ontology generation. We examine one coarse category of named entities, persons, and describe a method for automatically classifying person instances …...

We present a principled approach to the problem of con- necting a controlled document authoring system with a knowledge base. We start by describingclosed-world au- thoring situations, in which the knowledge base is used for constraining the possible documents and orientingthe user’s selections. Then we move to open-world author- ing …...

Ambiguity is very high for location names. For example, there are 23 cities named ‘Buffalo’ in the U.S. Country names such as ‘Canada’, ‘Brazil’ and ‘China’ are also city names in the USA. Almost every city has a Main Street or Broadway. Such ambiguity needs to be handled before we …...

We describe an LR parser of parts-of- speech (and punctuation labels) for Tree Adjoining Grammars (TAGs), that solves table conflicts in a greedy way, with lim- ited amount of backtracking. We evaluate the parser using the Penn Treebank show- ing that the method yield very fast parsers with at least …...

This paper presents a technique for discover- ing translationally equivalent texts. It is com- prised of the application of a matching algo- rithm at two different levels of analysis and a well-founded similarity score. This approach can be applied to any multilingual corpus us- ing any kind of translation lexicon; …...

This paper describes improved HMM-based word level alignment models for statistical machine translation. We present a method for using part of speech tag information to improve alignment accu- racy, and an approach to modeling fertility and cor- respondence to the empty word in an HMM align- ment model. We present …...

Most machine learning solutions to noun phrase coreference resolution recast the problem as a classification task. We ex- amine three potential problems with this reformulation, namely, skewed class dis- tributions, the inclusion of “hard” training instances, and the loss of transitivity in- herent in the original coreference relation. We show …...

In this paper, we evaluate a vari- ety of knowledge sources and super- vised learning algorithms for word sense disambiguation on SENSEVAL-2 and SENSEVAL-1 data. Our knowledge sources include the part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic relations. The learning al- gorithms evaluated …...

This paper investigates several augmented mixture models that are competitive alternatives to standard Bayesian models and prove to be very suitable to word sense disambiguation and related classifica- tion tasks. We present a new classification correc- tion technique that successfully addresses the prob- lem of under-estimation of infrequent classes in …...

We consider the problem of classifying doc- uments not by topic, but by overall senti- ment, e.g., determining whether a review is positive or negative. Using movie re- views as data, we find that standard ma- chine learning techniques definitively out- perform human-produced baselines. How- ever, the three machine learning …...

We demonstrate a problem with the stan- dard technique for learning probabilistic decision lists. We describe a simple, in- cremental algorithm that avoids this prob- lem, and show how to implement it effi- ciently. We also show a variation that adds thresholding to the standard sorting algo- rithm for decision …...

This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including second order classifier stacking, over 6 major structurally different base classifiers (enhanced Naïve Bayes, cosine, Bayes Ratio, decision lists, transformation- based learning and maximum variance …...

This paper separates conditional parameter estima- tion, which consistently raises test set accuracy on statistical NLP tasks, from conditional model struc- tures, such as the conditional Markov model used for maximum-entropy tagging, which tend to lower accuracy. Error analysis on part-of-speech tagging shows that the actual tagging errors made by …...

We describe the architecture of the AskMSR question answering system and systematically evaluate contributions of different system components to accuracy. The system differs from most question answering systems in its dependency on data redundancy rather than sophisticated linguistic analyses of either questions or candidate answers. Because a wrong an- swer …...

This paper presents several practical ways of incorporating linguistic structure into language models. A headword detector is first applied to detect the headword of each phrase in a sentence. A permuted headword trigram model (PHTM) is then generated from the annotated corpus. Finally, PHTM is extended to a cluster PHTM …...

A new almost-parsing language model incorporat- ing multiple knowledge sources that is based upon the concept of Constraint Dependency Grammars is presented in this paper. Lexical features and syn- tactic constraints are tightly integrated into a uni- form linguistic structure called a SuperARV that is associated with a word in …...

Ensemble methods are state of the art for many NLP tasks. Recent work by Banko and Brill (2001) suggests that this would not necessarily be true if very large training corpora were available. However, their results are limited by the simplic- ity of their evaluation task and individual classifiers. Our …...

This paper describes a bootstrapping al- gorithm called Basilisk that learns high- quality semantic lexicons for multiple cate- gories. Basilisk begins with an unannotated corpus and seed words for each semantic category, which are then bootstrapped to learn new words for each category. Basilisk hypothesizes the semantic class of a …...