Articles

We address the task of unsupervised POS tag- ging. We demonstrate that good results can be obtained using the robust EM-HMM learner when provided with good initial conditions, even with incomplete dictionaries. We present a family of algorithms to compute effective initial estimations p(t|w). We test the method on the …...

For centuries, the deep connection between languages has brought about major discover- ies about human communication. In this pa- per we investigate how this powerful source of information can be exploited for unsuper- vised language learning. In particular, we study the task of morphological segmentation of multiple languages. We present …...

Morphological disambiguation proceeds in 2 stages: (1) an analyzer provides all possible analyses for a given token and (2) a stochastic disambiguation module picks the most likely analysis in context. When the analyzer does not recognize a given token, we hit the prob- lem of unknowns. In large scale corpora, …...

This work describes an answer ranking engine for non-factoid questions built using a large online community-generated question-answer collection (Yahoo! Answers). We show how such collections may be used to effectively set up large supervised learning experiments. Furthermore we investigate a wide range of feature types, some exploiting NLP proces- sors, …...

We present a novel framework for the dis- covery and representation of general semantic relationships that hold between lexical items. We propose that each such relationship can be identified with a cluster of patterns that cap- tures this relationship. We give a fully unsu- pervised algorithm for pattern cluster discov- …...

The validity of semantic inferences depends on the contexts in which they are applied. We propose a generic framework for handling contextual considerations within applied in- ference, termed Contextual Preferences. This framework defines the various context-aware components needed for inference and their relationships. Contextual preferences extend and generalize previous notions, …...

Paraphrases have proved to be useful in many applications, including Machine Translation, Question Answering, Summarization, and In- formation Retrieval. Paraphrase acquisition methods that use a single monolingual corpus often produce only syntactic paraphrases. We present a method for obtaining surface para- phrases, using a 150GB (25 billion words) monolingual corpus. …...

This paper provides evidence that the use of more unlabeled data in semi-supervised learn- ing can improve the performance of Natu- ral Language Processing (NLP) tasks, such as part-of-speech tagging, syntactic chunking, and named entity recognition. We first pro- pose a simple yet powerful semi-supervised discriminative model appropriate for handling …...

Traditional wisdom holds that once docu- ments are turned into bag-of-words (unigram count) vectors, word orders are completely lost. We introduce an approach that, perhaps surprisingly, is able to learn a bigram lan- guage model from a set of bag-of-words docu- ments. At its heart, our approach is an EM …...

Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of decision trees (DT) and Transformation Based Learn- ing (TBL). In this work, we apply the ETL framework to four phrase chunking tasks: Por- tuguese noun phrase chunking, English base noun phrase chunking, English text …...

Online forum discussions often contain vast amounts of questions that are the focuses of discussions. Extracting contexts and answers together with the questions will yield not only a coherent forum summary but also a valu- able QA knowledge base. In this paper, we propose a general framework based on Con- …...

We address two problems in the field of au- tomatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating the result with real users. We use Reinforcement Learning (RL) to learn multimodal dialogue strategies by interaction with a simulated envi- ronment which …...

This work presents an agenda-based approach to improve the robustness of the dialog man- ager by using dialog examples and n-best recognition hypotheses. This approach sup- ports n-best hypotheses in the dialog man- ager and keeps track of the dialog state us- ing a discourse interpretation algorithm with the agenda …...

This paper describes how external resources can be used to improve parser performance for heavily lexicalised grammars, looking at both robustness and efficiency. In terms of robust- ness, we try using different types of external data to increase lexical coverage, and find that simple POS tags have the most effect, …...

Previous studies evaluate simulated dialog corpora using evaluation measures which can be automatically extracted from the dialog systems’ logs. However, the validity of these automatic measures has not been fully proven. In this study, we first recruit human judges to assess the quality of three simulated dia- log corpora and …...

Web search engines today typically show re- sults as a list of titles and short snippets that summarize how the retrieved documents are related to the query. However, recent research suggests that longer summaries can be prefer- able for certain types of queries. This pa- per presents empirical evidence that …...

We present a simple and effective semi- supervised method for training dependency parsers. We focus on the problem of lex- ical representation, introducing features that incorporate word clusters derived from a large unannotated corpus. We demonstrate the ef- fectiveness of the approach in a series of de- pendency parsing experiments …...

Synchronous Tree-Adjoining Grammar (STAG) is a promising formalism for syntax- aware machine translation and simultaneous computation of natural-language syntax and semantics. Current research in both of these areas is actively pursuing its incorporation. However, STAG parsing is known to be NP-hard due to the potential for intertwined correspondences between the …...

Conventional n-best reranking techniques of- ten suffer from the limited scope of the n- best list, which rules out many potentially good alternatives. We instead propose forest reranking, a method that reranks a packed for- est of exponentially many parses. Since ex- act inference is intractable with non-local fea- tures, …...

In this paper, we propose a novel string-to- dependency algorithm for statistical machine translation. With this new framework, we em- ploy a target dependency language model dur- ing decoding to exploit long distance word relations, which are unavailable with a tra- ditional n-gram language model. Our ex- periments show that …...