Articles

We describe a novel neural network archi- tecture for the problem of semantic role la- beling. Many current solutions are compli- cated, consist of several stages and hand- built features, and are too slow to be applied as part of real applications that require such semantic labels, partly because of …...

This paper presents a method for the auto- matic generation of a table-of-contents. This type of summary could serve as an effec- tive navigation tool for accessing informa- tion in long texts, such as books. To gen- erate a coherent table-of-contents, we need to capture both global dependencies across different …...

This paper examines whether a learning- based coreference resolver can be improved using semantic class knowledge that is au- tomatically acquired from a version of the Penn Treebank in which the noun phrases are labeled with their semantic classes. Ex- periments on the ACE test data show that a resolver …...

Semantic relatedness is a very important fac- tor for the coreference resolution task. To obtain this semantic information, corpus- based approaches commonly leverage pat- terns that can express a specific semantic relation. The patterns, however, are de- signed manually and thus are not necessar- ily the most effective ones in …...

We propose a novel approach to crosslingual language model (LM) adaptation based on bilingual Latent Semantic Analysis (bLSA). A bLSA model is introduced which enables latent topic distributions to be efficiently transferred across languages by enforcing a one-to-one topic correspondence during training. Using the proposed bLSA frame- work crosslingual LM …...

A Bloom filter (BF) is a randomised data structure for set membership queries. Its space requirements are significantly below lossless information-theoretic lower bounds but it produces false positives with some quantifiable probability. Here we explore the use of BFs for language modelling in statis- tical machine translation. We show how …...

Over the last fifty years, the “Big Five” model of personality traits has become a standard in psychology, and research has systematically documented correlations be- tween a wide range of linguistic variables and the Big Five traits. A distinct line of research has explored methods for automati- cally generating language …...

We address the task of unsupervised topic segmentation of speech data operating over raw acoustic information. In contrast to ex- isting algorithms for topic segmentation of speech, our approach does not require in- put transcripts. Our method predicts topic changes by analyzing the distribution of re- occurring acoustic patterns in …...

Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated inde- pendently. This paper proposes a novel it- erative reinforcement approach to simulta- neously extracting summary and keywords from single document under the assump- tion that the summary and …...

We investigate different feature sets for performing automatic sentence-level dis- course segmentation within a general ma- chine learning approach, including features derived from either finite-state or context- free annotations. We achieve the best re- ported performance on this task, and demon- strate that our SPADE-inspired context-free features are critical to …...

Topic segmentation and identification are of- ten tackled as separate problems whereas they are both part of topic analysis. In this article, we study how topic identification can help to improve a topic segmenter based on word reiteration. We first present an unsu- pervised method for discovering the topics of …...

We present an approach to query expan- sion in answer retrieval that uses Statisti- cal Machine Translation (SMT) techniques to bridge the lexical gap between ques- tions and answers. SMT-based query ex- pansion is done by i) using a full-sentence paraphraser to introduce synonyms in con- text of the entire …...

We propose a computational model of text reuse tailored for ancient literary texts, avail- able to us often only in small and noisy sam- ples. The model takes into account source alternation patterns, so as to be able to align even sentences with low surface similarity. We demonstrate its ability …...

Obtaining large volumes of inference knowl- edge, such as entailment rules, has become a major factor in achieving robust seman- tic processing. While there has been sub- stantial research on learning algorithms for such knowledge, their evaluation method- ology has been problematic, hindering fur- ther research. We propose a novel …...

Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed dif- ferently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adap- tation for sentiment classifiers, focusing on online reviews for different types of prod- ucts. …...

Recently, there has been a rise of in- terest in unsupervised detection of high- level semantic relations involving com- plex units, such as phrases and whole sentences. Typically such approaches are faced with two main obstacles: data sparseness and correctly generalizing from the examples. In this work, we describe the …...

This paper presents an application of PageR- ank, a random-walk model originally de- vised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess a given semantic prop- erty. The semantic properties we use for ex- emplifying the approach are positivity and negativity, two …...

While the average performance of statisti- cal parsers gradually improves, they still at- tach to many sentences annotations of rather low quality. The number of such sentences grows when the training and test data are taken from different domains, which is the case for major web applications such as in- …...

In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Infer- ence in the model is based on standard se- quence classification techniques using con- strained Viterbi to ensure consistent solu- tions. The primary advantage of such a model is …...

Deriving the polarity and strength of opinions is an important research topic, attracting sig- nificant attention over the last few years. In this work, to measure the strength and po- larity of an opinion, we consider the eco- nomic context in which the opinion is eval- uated, instead of using …...