Articles

We have explored the usefulness of incorporat- ing speech and discourse features in an automatic speech summarization system applied to meeting recordings from the ICSI Meetings corpus. By an- alyzing speaker activity, turn-taking and discourse cues, we hypothesize that such a system can out- perform solely text-based methods inherited from …...

The role of aggregation in natural lan- guage generation is to combine two or more linguistic structures into a single sentence. The task is crucial for generat- ing concise and readable texts. We present an efficient algorithm for automatically learning aggregation rules from a text and its related database. The …...

Ranked lists of output trees from syn- tactic statistical NLP applications fre- quently contain multiple repeated entries. This redundancy leads to misrepresenta- tion of tree weight and reduced informa- tion for debugging and tuning purposes. It is chiefly due to nondeterminism in the weighted automata that produce the re- sults. …...

We consider several empirical estimators for probabilistic context-free grammars, and show that the estimated grammars have the so-called consistency property, under the most general conditions. Our estimators include the widely applied ex- pectation maximization method, used to estimate probabilistic context-free gram- mars on the basis of unannotated corpora. This solves …...

We investigate the problem of training probabilistic context-free grammars on the basis of a distribution defined over an infinite set of trees, by minimizing the cross-entropy. This problem can be seen as a generalization of the well-known maximum likelihood estimator on (finite) tree banks. We prove an unexpected the- oretical …...

In this paper, we present a rule based model for morphological disambiguation of Turkish. The rules are generated by a novel decision list learning algorithm us- ing supervised training. Morphological ambiguity (e.g. lives = live+s or life+s) is a challenging problem for agglutinative languages like Turkish where close to half …...

We investigate prototype-driven learning for pri- marily unsupervised sequence modeling. Prior knowledge is specified declaratively, by provid- ing a few canonical examples of each target an- notation label. This sparse prototype information is then propagated across a corpus using distri- butional similarity features in a log-linear gener- ative model. On …...

We present a method for induction of con- cise and accurate probabilistic context- free grammars for efficient use in early stages of a multi-stage parsing technique. The method is based on the use of statis- tical tests to determine if a non-terminal combination is unobserved due to sparse data or …...

In order for relation extraction systems to obtain human-level performance, they must be able to incorporate relational pat- terns inherent in the data (for example, that one’s sister is likely one’s mother’s daughter, or that children are likely to attend the same college as their par- ents). Hand-coding such knowledge …...

This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extrac- tion. Our study reveals that the syntactic structure features embedded in a parse tree are very effective for relation extrac- tion and these features can be well cap- tured by the …...

Recent work in designing spoken dialogue systems has focused on using Reinforce- ment Learning to automatically learn the best action for a system to take at any point in the dialogue to maximize dia- logue success. While policy development is very important, choosing the best fea- tures to model the …...

We investigate using the PARADISE framework to develop predictive models of system performance in our spoken di- alogue tutoring system. We represent per- formance with two metrics: user satis- faction and student learning. We train and test predictive models of these met- rics in our tutoring system corpora. We predict …...

Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine transla- tion output, but are often very computa- tionally intensive. The complexity is ex- ponential in the size of individual gram- mar rules due to arbitrary re-orderings be- tween the two languages, and rules …...

We present an approach to statistical machine translation that combines ideas from phrase-based SMT and traditional grammar-based MT. Our system incor- porates the concept of multi-word trans- lation units into transfer of dependency structure snippets, and models and trains statistical components according to state- of-the-art SMT systems. Compliant with classical …...

We identify problems with the Penn Tree- bank that render it imperfect for syntax- based machine translation and propose methods of relabeling the syntax trees to improve translation quality. We develop a system incorporating a handful of relabel- ing strategies that yields a statistically sig- nificant improvement of 2.3 BLEU …...

The speed with which pronunciation dictio- naries can be bootstrapped depends on the ef- ficiency of learning algorithms and on the or- dering of words presented to the user. This pa- per presents an active-learning word selection strategy that is mindful of human limitations. Learning rates approach that of an …...

Recognition of tone and intonation is es- sential for speech recognition and lan- guage understanding. However, most ap- proaches to this recognition task have re- lied upon extensive collections of man- ually tagged data obtained at substantial time and financial cost. In this paper, we explore two approaches to tone …...

This paper investigates the feasibility of automated scoring of spoken English proficiency of non-native speakers. Unlike existing automated assessments of spoken English, our data consists of spontaneous spoken responses to complex test items. We perform both a quantitative and a qualitative analysis of these features using two different machine learning …...

In this paper we present a novel feature- enriched approach that learns to detect the conversation focus of threaded discus- sions by combining NLP analysis and IR techniques. Using the graph-based algo- rithm HITS, we integrate different fea- tures such as lexical similarity, poster trustworthiness, and speech act analysis of …...

In this paper we present an extension of a machine learning based coreference res- olution system which uses features in- duced from different semantic knowledge sources. These features represent knowl- edge mined from WordNet and Wikipedia, as well as information about semantic role labels. We show that semantic features in- …...