Articles

Many factors are thought to increase the chances of misrecognizing a word in ASR, including low frequency, nearby disfluencies, short duration, and being at the start of a turn. However, few of these factors have been for- mally examined. This paper analyzes a variety of lexical, prosodic, and disfluency factors …...

We outline the problem of ad hoc rules in treebanks, rules used for specific construc- tions in one data set and unlikely to be used again. These include ungeneralizable rules, erroneous rules, rules for ungrammatical text, and rules which are not consistent with the rest of the annotation scheme. Based …...

Morphologicalprocesses in Semitic languages deliver space-delimited words which intro- duce multiple, distinct, syntactic units into the structure of the input sentence. These words are in turn highly ambiguous, breaking the assumption underlying most parsers that the yield of a tree for a given sentence is known in advance. Here we …...

In this paper, we study the problem of sum- marizing email conversations. We first build a sentence quotation graph that captures the conversation structure among emails. We adopt three cohesion measures: clue words, semantic similarity and cosine similarity as the weight of the edges. Second, we use two graph-based summarization …...

Parse-tree paths are commonly used to incor- porate information from syntactic parses into NLP systems. These systems typically treat the paths as atomic (or nearly atomic) features; these features are quite sparse due to the im- mense variety of syntactic expression. In this paper, we propose a general method for …...

Statistical parsing of noun phrase (NP) struc- ture has been hampered by a lack of gold- standard data. This is a significant problem for CCGbank, where binary branching NP deriva- tions are often incorrect, a result of the auto- matic conversion from the Penn Treebank. We correct these errors in …...

The standard set of rules defined in Combina- tory Categorial Grammar (CCG) fails to pro- vide satisfactory analyses for a number of syn- tactic structures found in natural languages. These structures can be analyzed elegantly by augmenting CCG with a class of rules based on the combinator D (Curry and …...

To date, parsers have made limited use of se- mantic information, but there is evidence to suggest that semantic features can enhance parse disambiguation. This paper shows that semantic classes help to obtain significant im- provement in both parsing and PP attachment tasks. We devise a gold-standard sense- and parse …...

The paper presents a novel sentence trimmer in Japanese, which combines a non-statistical yet generic tree generation model and Con- ditional Random Fields (CRFs), to address improving the grammaticality of compres- sion while retaining its relevance. Experi- ments found that the present approach out- performs in grammaticality and in relevance …...

Online reviews are often accompanied with numerical ratings provided by users for a set of service or product aspects. We propose a statistical model which is able to discover corresponding topics in text and extract tex- tual evidence from reviews supporting each of these aspect ratings – a fundamental problem …...

This study presents a novel approach to the problem of system portability across differ- ent domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on Word- Net. The paper explores the challenges of sys- tem …...

Since facts or statements in a hedge or negated context typically appear as false positives, the proper handling of these language phenomena is of great importance in biomedical text min- ing. In this paper we demonstrate the impor- tance of hedge classification experimentally in two real life scenarios, namely the …...

The availability of databases of images labeled with keywords is necessary for developing and evaluating image annotation models. Dataset collection is however a costly and time con- suming task. In this paper we exploit the vast resource of images available on the web. We create a database of pictures that …...

We apply the hypothesis of “One Sense Per Discourse” (Yarowsky, 1995) to information extraction (IE), and extend the scope of “dis- course” from one single document to a cluster of topically-related documents. We employ a similar approach to propagate consistent event arguments across sentences and documents. Combining global evidence from …...

This paper proposes a framework for repre- senting the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of additive and multiplicative func- tions. Under this framework, we introduce a wide range of composition models which we evaluate empirically …...

There are many possible different semantic re- lationships between nominals. Classification of such relationships is an important and dif- ficult task (for example, the well known noun compound classification task is a special case of this problem). We propose a novel pat- tern clusters method for nominal relationship (NR) classification. …...

We propose the use of regular tree grammars (RTGs) as a formalism for the underspecified processing of scope ambiguities. By applying standard results on RTGs, we obtain a novel algorithm for eliminating equivalent readings and the first efficient algorithm for computing the best reading of a scope ambiguity. We also …...

We take a multi-pass approach to ma- chine translation decoding when using syn- chronous context-free grammars as the trans- lation model and n-gram language models: the first pass uses a bigram language model, and the resulting parse forest is used in the second pass to guide search with a trigram …...

Large-scale discriminative machine transla- tion promises to further the state-of-the-art, but has failed to deliver convincing gains over current heuristic frequency count systems. We argue that a principle reason for this failure is not dealing with multiple, equivalent transla- tions. We present a translation model which models derivations as a …...

We present a novel hierarchical prior struc- ture for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task across natural language data sets. The problem of transfer learning, where information gained in one learning task is used to improve perfor- mance …...