Articles

Discourse connectives (e.g. however, because) are terms that explicitly express discourse rela- tions in a coherent text. While a list of discourse connectives is useful for both theoretical and empirical research on discourse relations, few languages currently possess such a resource. In this article, we propose a new method that …...

Infographics, such as bar charts and line graphs, occur often in popular media and are a rich knowledge source that should be accessible to users. Unfortunately, information retrieval re- search has focused on the retrieval of text documents and images, with almost no attention specif- ically directed toward the retrieval …...

This paper addresses the problem of building concise, diverse and relevant lists of documents, which can be recommended to the participants of a conversation to fulfill their information needs without distracting them. These lists are retrieved periodically by submitting multiple implicit queries derived from the pronounced words. Each query is …...

We present a cross-lingual discourse relation analysis based on a parallel corpus with discourse information available only for one language. First, we conduct a corpus study to explore dif- ferences in discourse organization between Chinese and English, including differences in infor- mation packaging, implicit/explicit discourse expression divergence, and discourse connective …...

Genre classification has been found to improve performance in many applications of statistical NLP, including language modeling for spoken language, domain adaptation of statistical parsers, and machine translation. It has also been found to benefit retrieval of spoken or written docu- ments. At its base, however, classification assumes separability. This …...

Most text classification approaches model text at the lexical and syntactic level only, lacking do- main robustness and explainability. In tasks like sentiment analysis, such approaches can result in limited effectiveness if the texts to be classified consist of a series of arguments. In this paper, we claim that even …...

For languages such as English, several constituent-to-dependency conversion schemes are pro- posed to construct corpora for dependency parsing. It is hard to determine which scheme is better because they reflect different views of dependency analysis. We usually obtain dependen- cy parsers of different schemes by training with the specific corpus …...

The paper introduces an LR-based algorithm for efficient phrase structure parsing of morpholog- ically rich languages. The algorithm generalizes lexicalized parsing (Collins, 2003) by allowing a structured representation of the lexical items. Together with a discriminative weighting com- ponent (Collins, 2002), we show that this representation allows us to achieve …...

Recent work has shown success in learning word embeddings with neural network language models (NNLM). However, the majority of previous NNLMs represent each word with a single embedding, which fails to capture polysemy. In this paper, we address this problem by represent- ing words with multiple and sense-specific embeddings, which …...

The work presented here addresses the use of unmarked contexts in pattern-based nominal lexical semantic classification. We define unmarked contexts to be the counterposition of the class-indicatory, or marked, contexts. Its aim is to evaluate how unmarked contexts can be used to improve the accuracy and reliability of lexical semantic …...

Personal skill information on social media is at the core of many interesting applications. In this paper, we propose a factor graph based approach to automatically infer skills from per- sonal profile incorporated with both personal and skill connections. We first extract personal connections with similar academic and business background …...

The most widely used similarity measure in the field of natural language processing may be co- sine similarity. However, in the context of Twitter, the large scale of massive tweet data inevitably makes it expensive to perform cosine similarity computations among tremendous data samples. In this paper, we exploit binary …...

This paper considers the problem of finding topical shifts in documents and in particular at what information can be leveraged to identify them. Recent research on topical segmentation usually assumes that topical shifts in discourse are signalled by changes in vocabulary. This information, however, is not always a sufficient indicator …...

Linguistic accommodation is a recognised indicator of social power and social distance. How- ever, different individuals will vary their language to different degrees, and only a portion of this variance will be due to accommodation. This paper presents the Zelig Quotient, a method of normalising linguistic variation towards a particular …...

In this study we compare two machine translation devices on twelve machine translation medical- domain specific tasks, and two transliteration tasks, altogether involving twelve language pairs, including English-Chinese and English-Russian, which do not share the same scripts. We imple- mented an analogical device and compared its performance to the state-of-the-art …...

We investigate the use of generalized representations (POS, morphological analysis and word clusters) in phrase-based models and the N-gram-based Operation Sequence Model (OSM). Our integration enables these models to learn richer lexical and reordering patterns, consider wider contextual information and generalize better in sparse data conditions. When interpolating gen- eralized …...

Our previous work focuses on combining translation memory (TM) and statistical machine translation (SMT) when the TM database and the SMT training set are the same. However, the TM database will deviate from the SMT training set in the real task when time goes by. In this work, we concentrate …...

Arabic words are often ambiguous between name and non-name interpretations, frequently leading to incorrect name translations. We present a technique to disambiguate and transliter- ate names even if name interpretations do not exist or have relatively low probability distribu- tions in the parallel training corpus. The key idea comprises named …...

Machine Translation (MT) Quality Estimation (QE) aims to automatically measure the quality of MT system output without reference translations. In spite of the progress achieved in re- cent years, current MT QE systems are not capable of dealing with data coming from different train/test distributions or domains, and scenarios in …...

Authorship verification is the problem of answering the question whether or not a sample text document was written by a specific person, given a few other documents known to be authored by them. We propose a proximity based method for one-class classification that applies the Common N-Gram (CNG) dissimilarity measure. …...