Articles

In this paper, we investigate structured mod- els for document-level sentiment classifica- tion. When predicting the sentiment of a sub- jective document (e.g., as positive or nega- tive), it is well known that not all sentences are equally discriminative or informative. But identifying the useful sentences automatically is itself a …...

In this paper, we focus on the opinion tar- get extraction as part of the opinion min- ing task. We model the problem as an in- formation extraction task, which we address based on Conditional Random Fields (CRF). As a baseline we employ the supervised al- gorithm by Zhuang et …...

We present a novel approach to relation ex- traction that integrates information across doc- uments, performs global inference and re- quires no labelled text. In particular, we tackle relation extraction and entity identifi- cation jointly. We use distant supervision to train a factor graph model for relation ex- traction based …...

In this paper we introduce the new task of social event extraction from text. We distin- guish two broad types of social events depend- ing on whether only one or both parties are aware of the social contact. We annotate part of Automatic Content Extraction (ACE) data, and perform experiments …...

Named-entity recognition (NER) is an impor- tant task required in a wide variety of ap- plications. While rule-based systems are ap- pealing due to their well-known “explainabil- ity,” most, if not all, state-of-the-art results for NER tasks are based on machine learning techniques. Motivated by these results, we ex- plore …...

The research question treated in this paper is centered on the idea of exploiting rich re- sources of one language to enhance the per- formance of a mention detection system of an- other one. We successfully achieve this goal by projecting information from one language to another via a parallel …...

Confidence-Weighted linear classifiers (CW) and its successors were shown to perform well on binary and multiclass NLP prob- lems. In this paper we extend the CW ap- proach for sequence learning and show that it achieves state-of-the-art performance on four noun phrase chucking and named entity recog- nition tasks. We …...

In state-of-the-art approaches to information extraction (IE), dependency graphs constitute the fundamental data structure for syntactic structuring and subsequent knowledge elicita- tion from natural language documents. The top-performing systems in the BioNLP 2009 Shared Task on Event Extraction all shared the idea to use dependency structures generated by a variety …...

In this paper, we consider the problem of gen- erating candidate corrections for the task of correcting errors in text. We focus on the task of correcting errors in preposition usage made by non-native English speakers, using discriminative classifiers. The standard ap- proach to the problem assumes that the set …...

Automatic evaluation of Machine Translation (MT) quality is essential to developing high- quality MT systems. Various evaluation met- rics have been proposed, and BLEU is now used as the de facto standard metric. How- ever, when we consider translation between distant language pairs such as Japanese and English, most popular …...

Electronic dictionaries covering all natural language levels are very relevant for the hu- man use as well as for the automatic processing use, namely those constructed with respect to international standards. Such dictionaries are characterized by a complex structure and an important access time when using a querying system. However, …...

We present PEM, the first fully automatic met- ric to evaluate the quality of paraphrases, and consequently, that of paraphrase generation systems. Our metric is based on three crite- ria: adequacy, fluency, and lexical dissimilar- ity. The key component in our metric is a ro- bust and shallow semantic similarity …...

The reliable extraction of knowledge from text requires an appropriate treatment of the time at which reported events take place. Unfortu- nately, there are very few annotated data sets that support the development of techniques for event time-stamping and tracking the progres- sion of time through a narrative. In this …...

Unknown words are a hindrance to the perfor- mance of hand-crafted computational gram- mars of natural language. However, words with incomplete and incorrect lexical entries pose an even bigger problem because they can be the cause of a parsing failure despite being listed in the lexicon of the grammar. Such …...

This paper proposes a method for automat- ically inserting commas into Japanese texts. In Japanese sentences, commas play an im- portant role in explicitly separating the con- stituents, such as words and phrases, of a sen- tence. The method can be used as an ele- mental technology for natural language …...

This paper proposes a unified framework for zero anaphora resolution, which can be di- vided into three sub-tasks: zero anaphor detec- tion, anaphoricity determination and antecedent identification. In particular, all the three sub-tasks are addressed using tree ker- nel-based methods with appropriate syntactic parse tree structures. Experimental results on a …...

We explore the task of automatically classify- ing dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service. In particular, we investi- gate the effectiveness of various features and machine learners for this task. While a sim- ple bag-of-words approach provides a solid baseline, we …...

Part-of-speech (POS) tag distributions are known to exhibit sparsity — a word is likely to take a single predominant tag in a corpus. Recent research has demonstrated that incor- porating this sparsity constraint improves tag- ging accuracy. However, in existing systems, this expansion come with a steep increase in model …...

We show that the standard beam-search al- gorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-tagging, achieving a significant speed im- provement. Such decoding is enabled by: (1) separating full word features from par- tial …...

This paper proposes a fast and simple unsuper- vised word segmentation algorithm that uti- lizes the local predictability of adjacent char- acter sequences, while searching for a least- effort representation of the data. The model uses branching entropy as a means of con- straining the hypothesis space, in order to …...