Articles

In this paper, we introduce a methodology for analyzing judgment opinions. We de- fine a judgment opinion as consisting of a valence, a holder, and a topic. We decom- pose the task of opinion analysis into four parts: 1) recognizing the opinion; 2) iden- tifying the valence; 3) identifying the …...

Dialog act (DA) tags are useful for many applications in natural language process- ing and automatic speech recognition. In this work, we introduce hidden backoff models (HBMs) where a large generalized backoff model is trained, using an embed- ded expectation-maximization (EM) pro- cedure, on data that is partially observed. We …...

We present an integrated probabilistic model for Japanese syntactic and case structure analysis. Syntactic and case structure are simultaneously analyzed based on wide-coverage case frames that are constructed from a huge raw corpus in an unsupervised manner. This model se- lects the syntactic and case structure that has the highest …...

We present a two stage parser that recov- ers Penn Treebank style syntactic analy- ses of new sentences including skeletal syntactic structure, and, for the first time, both function tags and empty categories. The accuracy of the first-stage parser on the standard Parseval metric matches that of the (Collins, 2003) …...

We present a PCFG parsing algorithm that uses a multilevel coarse-to-fine (mlctf) scheme to improve the effi- ciency of search for the best parse. Our approach requires the user to spec- ify a sequence of nested partitions or equivalence classes of the PCFG non- terminals. We define a sequence of …...

We develop dependency parsers for Ara- bic, English, Chinese, and Czech using Bayes Point Machines, a training algo- rithm which is as easy to implement as the perceptron yet competitive with large margin methods. We achieve results com- parable to state-of-the-art in English and Czech, and report the first directed …...

We present a simple, but surprisingly ef- fective, method of self-training a two- phase parser-reranker system using read- ily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f-score of …...

We propose a solution to the annotation bottleneck for statistical parsing, by ex- ploiting the lexicalized nature of Combi- natory Categorial Grammar (CCG). The parsing model uses predicate-argument dependencies for training, which are de- rived from sequences of CCG lexical cate- gories rather than full derivations. A sim- ple method …...

Recent years have seen increasing re- search on extracting and using temporal information in natural language applica- tions. However most of the works found in the literature have focused on identi- fying and understanding temporal expres- sions in newswire texts. In this paper we report our work on anchoring tempo- …...

Recently, discriminative word alignment methods have achieved state-of-the-art accuracies by extend- ing the range of information sources that can be easily incorporated into aligners. The chief advan- tage of a discriminative framework is the ability to score alignments based on arbitrary features of the matching word tokens, including orthographic form, …...

This paper shows that two uncertainty- based active learning methods, combined with a maximum entropy model, work well on learning English verb senses. Data analysis on the learning process, based on both instance and feature levels, suggests that a careful treatment of feature extraction is important for the active learning …...

We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the stan- dard practice of intersecting predictions of independently-trained models, joint train- ing provides a 32% reduction in …...

This paper presents a new approach to combining outputs of existing word align- ment systems. Each alignment link is rep- resented with a set of feature functions extracted from linguistic features and in- put alignments. These features are used as the basis of alignment decisions made by a maximum entropy …...

We address the problem of unknown word sense detection: the identification of cor- pus occurrences that are not covered by a given sense inventory. We model this as an instance of outlier detection, using a simple nearest neighbor-based approach to measuring the resemblance of a new item to a training …...

Discriminative probabilistic models are very popular in NLP because of the latitude they afford in designing features. But training involves complex trade-offs among weights, which can be dangerous: a few highly- indicative features can swamp the contribution of many individually weaker features, causing their weights to be undertrained. Such a …...

Named Entity recognition (NER) is an im- portant part of many natural language pro- cessing tasks. Most current approaches employ machine learning techniques and require supervised data. However, many languages lack such resources. This paper presents an algorithm to automatically dis- cover Named Entities (NEs) in a resource free language, …...

Named Entity Recognition (NER) is a fundamental task in text mining and nat- ural language understanding. Current ap- proaches to NER (mostly based on super- vised learning) perform well on domains similar to the training domain, but they tend to adapt poorly to slightly different domains. We present several strategies …...

Deidentification of clinical records is a crucial step before these records can be distributed to non-hospital researchers. Most approaches to deidentification rely heavily on dictionaries and heuristic rules; these approaches fail to remove most per- sonal health information (PHI) that cannot be found in dictionaries. They also can fail to …...

This paper shows that inference rules with temporal constraints can be acquired by us- ing verb-verb co-occurrences in Japanese coordinated sentences and verb-noun co- occurrences. For example, our unsuper- vised acquisition method could obtain the inference rule “If someone enforces a law, usually someone enacts the law at the same …...

This paper advocates a new architecture for tex- tual inference in which finding a good alignment is separated from evaluating entailment. Current ap- proaches to semantic inference in question answer- ing and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to …...