Articles

We propose a spectral approach for un- supervised constituent parsing that comes with theoretical guarantees on latent struc- ture recovery. Our approach is grammar- less – we directly learn the bracketing structure of a given sentence without us- ing a grammar model. The main algorithm is based on lifting the …...

We introduce a provably correct learning algorithm for latent-variable PCFGs. The algorithm relies on two steps: first, the use of a matrix-decomposition algorithm ap- plied to a co-occurrence matrix estimated from the parse trees in a training sample; second, the use of EM applied to a convex objective derived from …...

We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation. Our model incorporates heterogeneous re- lational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia ab- stracts. For efficient inference over tax- onomy …...

We present an unsupervised method for in- ducing verb classes from verb uses in giga- word corpora. Our method consists of two clustering steps: verb-specific seman- tic frames are first induced by clustering verb uses in a corpus and then verb classes are induced by clustering these frames. By taking …...

This paper introduces FactChecker, language-aware approach to truth-finding. FactChecker differs from prior approaches in that it does not rely on iterative peer voting, instead it leverages language to infer believability of fact candidates. In particular, FactChecker makes use of lin- guistic features to detect if a given source objectively states …...

In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distribu- tional thesauri based networks from data at different time points and cluster each of them separately to …...

Cross-narrative temporal ordering of med- ical events is essential to the task of gen- erating a comprehensive timeline over a patient’s history. We address the prob- lem of aligning multiple medical event se- quences, corresponding to different clin- ical narratives, comparing the following approaches: (1) A novel weighted finite state …...

We propose a robust answer reranking model for non-factoid questions that inte- grates lexical semantics with discourse in- formation, driven by two representations of discourse: a shallow representation cen- tered around discourse markers, and a deep one based on Rhetorical Structure Theory. We evaluate the proposed model on two corpora …...

A typical knowledge-based question an- swering (KB-QA) system faces two chal- lenges: one is to transform natural lan- guage questions into their meaning repre- sentations (MRs); the other is to retrieve answers from knowledge bases (KBs) us- ing generated MRs. Unlike previous meth- ods which treat them in a cascaded …...

Answering natural language questions us- ing the Freebase knowledge base has re- cently been explored as a platform for ad- vancing the state of the art in open do- main semantic parsing. Those efforts map questions to sophisticated meaning repre- sentations that are then attempted to be matched against viable …...

We present WiBi, an approach to the automatic creation of a bitaxonomy for Wikipedia, that is, an integrated taxon- omy of Wikipage pages and categories. We leverage the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments show higher quality and …...

Following the works of Carletta (1996) and Artstein and Poesio (2008), there is an increasing consensus within the field that in order to properly gauge the reliability of an annotation effort, chance-corrected measures of inter-annotator agreement should be used. With this in mind, it is striking that virtually all evaluations …...

We study the use of temporal information in the form of timelines to enhance multi- document summarization. We employ a fully automated temporal processing sys- tem to generate a timeline for each in- put document. We derive three features from these timelines, and show that their use in supervised summarization …...

Update summarization is a form of multi- document summarization where a document set must be summarized in the context of other documents assumed to be known. Efficient update summarization must focus on identify- ing new information and avoiding repetition of known information. In Query-focused summa- rization, the task is to …...

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture→exacerbate desertification from the web using se- mantic relation (between nouns), context, and association features. Experiments show that our method outperforms base- lines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like …...

Multi-document summarization (MDS) systems have been designed for short, un- structured summaries of 10-15 documents, and are inadequate for larger document collections. We propose a new approach to scaling up summarization called hierar- chical summarization, and present the first implemented system, SUMMA. SUMMA produces a hierarchy of relatively short summaries, …...

ive Summarization Daniele Pighin Google Inc. biondo@google.com Marco Cornolti∗ University of Pisa, Italy cornolti@di.unipi.it Enrique Alfonseca Google Inc. ealfonseca@google.com Katja Filippova Google Inc. katjaf@google.com Abstract Abstractive text summarization of news requires a way of representing events, such as a collection of pattern clusters in which every cluster represents an event …...

We propose a novel learning approach for statistical machine translation (SMT) that allows to extract supervision signals for structured learning from an extrinsic re- sponse to a translation input. We show how to generate responses by grounding SMT in the task of executing a seman- tic parse of a translated …...

In this paper we present new research in translation assistance. We describe a sys- tem capable of translating native language (L1) fragments to foreign language (L2) fragments in an L2 context. Practical ap- plications of this research can be framed in the context of second language learning. The type of …...

We present an adaptive translation qual- ity estimation (QE) method to predict the human-targeted translation error rate (HTER) for a document-specific machine translation model. We first introduce fea- tures derived internal to the translation de- coding process as well as externally from the source sentence analysis. We show the effectiveness …...