Articles

The well-studied supervised Relation Extraction algorithms require training data that is accurate and has good coverage. To obtain such a gold standard, the common practice is to do independent double annotation followed by adjudication. This takes significantly more human effort than annotation done by a single annotator. We do a …...

In this paper we deal with Named En- tity Recognition (NER) on transcriptions of French broadcast data. Two aspects make the task more difficult with respect to previ- ous NER tasks: i) named entities annotated used in this work have a tree structure, thus the task cannot be tackled as …...

We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adap- tation setting for which we have no labeled training data in the target domain. To fa- cilitate evaluation, we obtain annotations for articles in four topical groups, allow- ing annotators to identify domain-specific entity types in addition …...

Nowadays, there are large amounts of data available to train statistical machine trans- lation systems. However, it is not clear whether all the training data actually help or not. A system trained on a subset of such huge bilingual corpora might outperform the use of all the bilingual data. This …...

In this paper we investigate the use of character-level translation models to sup- port the translation from and to under- resourced languages and textual domains via closely related pivot languages. Our ex- periments show that these low-level models can be successful even with tiny amounts of training data. We test …...

We estimate the parameters of a phrase- based statistical machine translation sys- tem from monolingual corpora instead of a bilingual parallel corpus. We extend exist- ing research on bilingual lexicon induction to estimate both lexical and phrasal trans- lation probabilities for MT-scale phrase- tables. We propose a novel algorithm to …...

We present work on linking events and flu- ents (i.e., relations that hold for certain periods of time) to temporal information in text, which is an important enabler for many applications such as timelines and reasoning. Previous research has mainly focused on temporal links for events, and we extend that …...

The search space of Phrase-Based Statisti- cal Machine Translation (PBSMT) systems can be represented under the form of a di- rected acyclic graph (lattice). The quality of this search space can thus be evaluated by computing the best achievable hypoth- esis in the lattice, the so-called oracle hy- pothesis. For …...

This work proposes to adapt an existing general SMT model for the task of translat- ing queries that are subsequently going to be used to retrieve information from a tar- get language collection. In the scenario that we focus on access to the document collec- tion itself is not available …...

In this paper, we examined click patterns produced by users of Yahoo! search engine when prompting definition questions. Reg- ularities across these click patterns are then utilized for constructing a large and hetero- geneous training corpus for answer rank- ing. In a nutshell, answers are extracted from clicked web-snippets originating …...

In Information Retrieval (IR) in general and Question Answering (QA) in particu- lar, queries and relevant textual content of- ten significantly differ in their properties and are therefore difficult to relate with tra- ditional IR methods, e.g. key-word match- ing. In this paper we describe an algorithm that addresses this …...

We introduce a new approach to transition- based dependency parsing in which the parser does not directly construct a depen- dency structure, but rather an undirected graph, which is then converted into a di- rected dependency tree in a post-processing step. This alleviates error propagation, since undirected parsers do not …...

Hungarian is a stereotype of morpholog- ically rich and non-configurational lan- guages. Here, we introduce results on de- pendency parsing of Hungarian that em- ploy a 80K, multi-domain, fully manu- ally annotated corpus, the Szeged Depen- dency Treebank. We show that the results achieved by state-of-the-art data-driven parsers on Hungarian …...

A serious bottleneck of comparative parser evaluation is the fact that different parsers subscribe to different formal frameworks and theoretical assumptions. Converting outputs from one framework to another is less than optimal as it easily introduces noise into the process. Here we present a principled protocol for evaluating parsing results …...

We introduce two ways to detect entail- ment using distributional semantic repre- sentations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat |= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog |= animal). Our …...

We introduce two Bayesian models for un- supervised semantic role labeling (SRL) task. The models treat SRL as clustering of syntactic signatures of arguments with clusters corresponding to semantic roles. The first model induces these clusterings independently for each predicate, exploit- ing the Chinese Restaurant Process (CRP) as a prior. …...

We describe a novel method that extracts paraphrases from a bitext, for both the source and target languages. In order to reduce the search space, we decom- pose the phrase-table into sub-phrase-tables and construct separate clusters for source and target phrases. We convert the clus- ters into graphs, add smoothing/syntactic- …...

A major focus of current work in distri- butional models of semantics is to con- struct phrase representations composition- ally from word representations. However, the syntactic contexts which are modelled are usually severely limited, a fact which is reflected in the lexical-level WSD-like evaluation methods used. In this paper, we …...

In this paper we first describe the technol- ogy of automatic annotation transformation, which is based on the annotation adaptation algorithm (Jiang et al., 2009). It can auto- matically transform a human-annotated cor- pus from one annotation guideline to another. We then propose two optimization strategies, iterative training and predict-self …...

In statistical machine translation, minimum error rate training (MERT) is a standard method for tuning a single weight with regard to a given development data. However, due to the diversity and uneven distribution of source sentences, there are two problems suffered by this method. First, its performance is highly dependent …...