Articles

This work concerns automatic topic segmen- tation of email conversations. We present a corpus of email threads manually annotated with topics, and evaluate annotator reliabil- ity. To our knowledge, this is the first such email corpus. We show how the existing topic segmentation models (i.e., Lexical Chain Seg- menter (LCSeg) …...

Several recent discourse parsers have em- ployed fully-supervised machine learning ap- proaches. These methods require human an- notators to beforehand create an extensive training corpus, which is a time-consuming and costly process. On the other hand, un- labeled data is abundant and cheap to col- lect. In this paper, we …...

In this paper, we investigate how modeling content structure can benefit text analysis ap- plications such as extractive summarization and sentiment analysis. This follows the lin- guistic intuition that rich contextual informa- tion should be useful in these tasks. We present a framework which combines a su- pervised text analysis …...

Existing graph-based ranking methods for keyphrase extraction compute a single impor- tance score for each word via a single ran- dom walk. Motivated by the fact that both documents and words can be represented by a mixture of semantic topics, we propose to decompose traditional random walk into mul- tiple …...

Multi-category bootstrapping algorithms were developed to reduce semantic drift. By ex- tracting multiple semantic lexicons simultane- ously, a category’s search space may be re- stricted. The best results have been achieved through reliance on manually crafted negative categories. Unfortunately, identifying these categories is non-trivial, and their use shifts the unsupervised …...

Seed sampling is critical in semi-supervised learning. This paper proposes a clustering- based stratified seed sampling approach to semi-supervised learning. First, various clus- tering algorithms are explored to partition the unlabeled instances into different strata with each stratum represented by a center. Then, diversity-motivated intra-stratum sampling is adopted to choose …...

Information-extraction (IE) research typically focuses on clean-text inputs. However, an IE engine serving real applications yields many false alarms due to less-well-formed input. For example, IE in a multilingual broadcast processing system has to deal with inaccu- rate automatic transcription and translation. The resulting presence of non-target-language text in this …...

Polysemy is a major characteristic of natu- ral languages. Like words, syntactic forms can have several meanings. Understanding the correct meaning of a syntactic form is of great importance to many NLP applications. In this paper we address an important type of syn- tactic polysemy – the multiple possible senses …...

We show that jointly performing semantic role labeling (SRL) on bitext can improve SRL results on both sides. In our approach, we use monolingual SRL systems to produce ar- gument candidates for predicates in bitext at first. Then, we simultaneously generate SRL results for two sides of bitext using our …...

This paper presents a method for the auto- matic discovery of MANNER relations from text. An extended definition of MANNER is proposed, including restrictions on the sorts of concepts that can be part of its domain and range. The connections with other relations and the lexico-syntactic patterns that encode MANNER …...

Strong indications of perspective can often come from collocations of arbitrary length; for example, someone writing get the government out of my X is typically expressing a conserva- tive rather than progressive viewpoint. How- ever, going beyond unigram or bigram features in perspective classification gives rise to prob- lems of …...

Syntax-based translation models should in principle be efficient with polynomially-sized search space, but in practice they are often embarassingly slow, partly due to the cost of language model integration. In this paper we borrow from phrase-based decoding the idea to generate a translation incrementally left-to-right, and show that for tree-to-string …...

In many applications, replacing a complex word form by its stem can reduce sparsity, re- vealing connections in the data that would not otherwise be apparent. In this paper, we focus on prefix verbs: verbs formed by adding a pre- fix to an existing verb stem. A prefix verb is …...

We present three novel methods of compactly storing very large n-gram language models. These methods use substantially less space than all known approaches and allow n-gram probabilities or counts to be retrieved in con- stant time, at speeds comparable to modern language modeling toolkits. Our basic ap- proach generates an …...

Representing documents by vectors that are independent of language enhances machine translation and multilingual text categoriza- tion. We use discriminative training to create a projection of documents from multiple lan- guages into a single translingual vector space. We explore two variants to create these pro- jections: Oriented Principal Component Anal- …...

Models of latent document semantics such as the mixture of multinomials model and La- tent Dirichlet Allocation have received sub- stantial attention for their ability to discover topical semantics in large collections of text. In an effort to apply such models to noisy optical character recognition (OCR) text out- put, …...

Automated essay scoring is one of the most important educational applications of natural language processing. Recently, researchers have begun exploring methods of scoring es- says with respect to particular dimensions of quality such as coherence, technical errors, and relevance to prompt, but there is rela- tively little work on modeling …...

The problem of automatically classifying the gender of a blog author has important appli- cations in many commercial domains. Exist- ing systems mainly use features such as words, word classes, and POS (part-of- speech) n-grams, for classification learning. In this paper, we propose two new techniques to improve the current …...

We define the crouching Dirichlet, hidden Markov model (CDHMM), an HMM for part- of-speech tagging which draws state prior dis- tributions for each local document context. This simple modification of the HMM takes advantage of the dichotomy in natural lan- guage between content and function words. In contrast, a standard …...

Many sequence labeling tasks in NLP require solving a cascade of segmentation and tag- ging subtasks, such as Chinese POS tagging, named entity recognition, and so on. Tradi- tional pipeline approaches usually suffer from error propagation. Joint training/decoding in the cross-product state space could cause too many parameters and high …...