Articles

Manual evaluation of translation quality is generally thought to be excessively time consuming and expensive. We explore a fast and inexpensive way of doing it using Amazon’s Mechanical Turk to pay small sums to a large number of non-expert an- notators. For $10 we redundantly recre- ate judgments from a …...

One of the most desired information types when planning a trip to some place is the knowledge of transport, roads and geographical connectedness of prominent sites in this place. While some transport companies or repositories make some of this information accessible, it is not easy to find, and the majority …...

In this paper, we address the issue of au- tomatic extending lexical resources by ex- ploiting existing knowledge repositories. In particular, we deal with the new task of linking FrameNet and Wikipedia us- ing a word sense disambiguation system that, for a given pair frame – lexical unit (F, l), …...

Keyphrases are widely used as a brief summary of documents. Since man- ual assignment is time-consuming, vari- ous unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the docu- ment, but also have …...

A significant portion of the world’s text is tagged by readers on social bookmark- ing websites. Credit attribution is an in- herent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution …...

Combining information extraction sys- tems yields significantly higher quality re- sources than each system in isolation. In this paper, we generalize such a mixing of sources and features in a framework called Ensemble Semantics. We show very large gains in entity extraction by combining state-of-the-art distributional and pattern- based systems …...

We present a method to align words in a bitext that combines elements of a tra- ditional statistical approach with linguis- tic knowledge. We demonstrate this ap- proach for Arabic-English, using an align- ment lexicon produced by a statistical word aligner, as well as linguistic re- sources ranging from an …...

In this work, we propose two extensions of standard word lexicons in statistical ma- chine translation: A discriminative word lexicon that uses sentence-level source in- formation to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allow- ing for a more …...

We present a machine translation frame- work that can incorporate arbitrary fea- tures of both input and output sentences. The core of the approach is a novel de- coder based on lattice parsing with quasi- synchronous grammar (Smith and Eis- ner, 2006), a syntactic formalism that does not require source …...

We describe a novel approach for syntax- based statistical MT, which builds on a variant of tree adjoining grammar (TAG). Inspired by work in discriminative depen- dency parsing, the key idea in our ap- proach is to allow highly flexible reorder- ing operations during parsing, in combina- tion with a …...

This paper investigates a new task, subjec- tivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are be- ing used with objective senses. We pro- vide empirical evidence that SWSD is more feasible than full …...

This paper studies sentiment analysis of condi- tional sentences. The aim is to determine whether opinions expressed on different topics in a conditional sentence are positive, negative or neutral. Conditional sentences are one of the commonly used language constructs in text. In a typical document, there are around 8% of …...

This paper presents a parse-and-paraphrase pa- radigm to assess the degrees of sentiment for product reviews. Sentiment identification has been well studied; however, most previous work provides binary polarities only (positive and negative), and the polarity of sentiment is simply reversed when a negation is detected. The extraction of lexical …...

This work investigates design choices in modeling a discourse scheme for im- proving opinion polarity classification. For this, two diverse global inference paradigms are used: a supervised collec- tive classification framework and an un- supervised optimization framework. Both approaches perform substantially better than baseline approaches, establishing the efficacy of the …...

Information Extraction (IE) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. Of- ten, however, role fillers occur in clauses that are not directly linked to an event word. We present a new model for event extraction …...

Many named entities contain other named entities inside them. Despite this fact, the field of named entity recognition has al- most entirely ignored nested named en- tity recognition, but due to technological, rather than ideological reasons. In this pa- per, we present a new technique for rec- ognizing nested named …...

Traditionally, machine learning ap- proaches for information extraction require human annotated data that can be costly and time-consuming to produce. However, in many cases, there already exists a database (DB) with schema related to the desired output, and records related to the expected input text. We present a conditional random …...

Because of the importance of protein- protein interaction (PPI) extraction from text, many corpora have been proposed with slightly differing definitions of pro- teins and PPI. Since no single corpus is large enough to saturate a machine learn- ing system, it is necessary to learn from multiple different corpora. In …...

We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernel- based machine, e.g. Support Vector Ma- chines (SVMs). In particular, our min- ing algorithm selects the most relevant fea- tures based on SVM estimated …...

Current methods of using lexical features in machine translation have difficulty in scaling up to realistic MT tasks due to a prohibitively large number of parame- ters involved. In this paper, we propose methods of using new linguistic and con- textual features that do not suffer from this problem and …...