Articles

A Chinese news summarization method is proposed in order to help humans deal with the message services of news briefs broadcast over cell phones. The problem to be solved here is unique because a strict length limit (69 or 45 characters) is imposed on the summaries for the message service. …...

In this paper, we take Determinative-Measure Compounds as an example to demonstrate how the E-HowNet semantic composition mechanism works in deriving the sense representation for a newly coined determinative-measure (DM) compound. First, we define the sense of a closed set of each individual determiner and measure word in E-HowNet representation …...

We propose a version of Inversion Transduction Grammar (ITG) model with IBM-style notation of fertility to improve word-alignment performance. In our approach, binary context-free grammar rules of the source language, accompanied by orientation preferences of the target language and fertilities of words, are leveraged to construct a syntax-based statistical translation …...

Many applications in the context of natural language processing or information retrieval may be largely improved if they were able to fully exploit the rich semantic information an- notated in high-quality, publicly available re- sources such as the FrameNet and the Word- Net databases. Nevertheless, the practical use of similar …...

This paper presents a method for automatic topic identification using an encyclopedic graph derived from Wikipedia. The sys- tem is found to exceed the performance of previously proposed machine learning algo- rithms for topic identification, with an annota- tion consistency comparable to human anno- tations. 1...

We present a kernel-based approach for fine- grained classification of named entities. The only training data for our algorithm is a few manually annotated entities for each class. We defined kernel functions that implicitly map entities, represented by aggregating all con- texts in which they occur, into a latent seman- …...

Experimenting with different mathematical objects for text representation is an important step of building text classification models. In order to be efficient, such objects of a for- mal model, like vectors, have to reasonably re- produce language-related phenomena such as word meaning inherent in index terms. We in- troduce an …...

We compare two different types of extraction patterns for automatically deriving semantic information from text: lexical patterns, built from words and word class information, and dependency patterns with syntactic informa- tion obtained from a full parser. We are partic- ularly interested in whether the richer linguis- tic information provided by …...

Clustering is crucial for many NLP tasks and applications. However, evaluating the results of a clustering algorithm is hard. In this paper we focus on the evaluation setting in which a gold standard solution is available. We discuss two existing information theory based mea- sures, V and VI, and show …...

As supervised machine learning methods are increasingly used in language technology, the need for high-quality annotated language data becomes imminent. Active learning (AL) is a means to alleviate the burden of annotation. This paper addresses the problem of knowing when to stop the AL process without having the human annotator …...

We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowl- edge and how …...

The average results obtained by unsupervised statistical parsers have greatly improved in the last few years, but on many specific sentences they are of rather low quality. The output of such parsers is becoming valuable for vari- ous applications, and it is radically less expen- sive to create than manually …...

This paper presents novel improvements to the induction of translation lexicons from monolingual corpora using multilin- gual dependency parses. We introduce a dependency-based context model that in- corporates long-range dependencies, vari- able context sizes, and reordering. It pro- vides a 16% relative improvement over the baseline approach that uses a …...

English pronouns like he and they reliably re- flect the gender and number of the entities to which they refer. Pronoun resolution systems can use this fact to filter noun candidates that do not agree with the pronoun gender. In- deed, broad-coverage models of noun gender have proved to be …...

In this paper we investigate the task of auto- matic generation of slide presentations from academic papers, focusing initially on slide to paper alignment. We compare and eval- uate four different alignment systems which utilize various combinations of methods used widely in other alignment and question an- swering approaches, such …...

Recent advances in statistical machine translation have used beam search for approximate NP-complete inference within probabilistic translation models. We present an alternative approach of sampling from the posterior distribution defined by a translation model. We define a novel Gibbs sampler for sampling translations given a source sentence and show that …...

In this paper we address the problem of identifying reciprocal relationships in English. In particular we introduce an algorithm that semi-automatically discovers patterns encod- ing reciprocity based on a set of simple but effective pronoun templates. Using a set of most frequently occurring patterns, we extract pairs of reciprocal pattern …...

Specifying an appropriate feature space is an important aspect of achieving good perfor- mance when designing systems based upon learned classifiers. Effectively incorporat- ing information regarding semantically related words into the feature space is known to pro- duce robust, accurate classifiers and is one ap- parent motivation for efforts to …...

Sets of lexical items sharing a significant aspect of their meaning (concepts) are fun- damental for linguistics and NLP. Unsuper- vised concept acquisition algorithms have been shown to produce good results, and are preferable over manual preparation of con- cept resources, which is labor intensive, er- ror prone and somewhat …...

Vector space models of word meaning typi- cally represent the meaning of a word as a vec- tor computed by summing over all its corpus occurrences. Words close to this point in space can be assumed to be similar to it in meaning. But how far around this point does …...