Articles

Reading proficiency is a fundamen- tal component of language competency. However, finding topical texts at an appro- priate reading level for foreign and sec- ond language learners is a challenge for teachers. This task can be addressed with natural language processing technology to assess reading level. Existing measures of reading …...

We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a sec- ond “reranking” model is then used to choose an utterance from …...

We have established a phonotactic lan- guage model as the solution to spoken language identification (LID). In this framework, we define a single set of acoustic tokens to represent the acoustic activities in the world’s spoken languages. A voice tokenizer converts a spoken document into a text-like document of acoustic …...

A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related, and …...

In this paper, we examine the task of extracting a set of biographic facts about target individuals from a collection of Web pages. We automatically anno- tate training text with positive and negative exam- ples of fact extractions and train Rote, Na¨ıve Bayes, and Conditional Random Field extraction models for …...

We present a version of Inversion Trans- duction Grammar where rule probabili- ties are lexicalized throughout the syn- chronous parse tree, along with pruning techniques for efficient training. Align- ment results improve over unlexicalized ITG on short sentences for which full EM is feasible, but pruning seems to have a …...

This paper proposes an alignment adaptation approach to improve domain-specific (in-domain) word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain …...

We present a framework for word align- ment based on log-linear models. All knowledge sources are treated as feature functions, which depend on the source langauge sentence, the target language sentence and possible additional vari- ables. Log-linear models allow statis- tical alignment models to be easily ex- tended by incorporating …...

The paper presents the Position Specific Posterior Lattice, a novel representation of automatic speech recognition lattices that naturally lends itself to efficient in- dexing of position information and subse- quent relevance ranking of spoken docu- ments using proximity. In experiments performed on a collec- tion of lecture recordings — MIT …...

Sentence boundary detection in speech is important for enriching speech recogni- tion output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hid- den Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting …...

In this work, we provide an empiri- cal analysis of differences in word use between genders in telephone conversa- tions, which complements the consid- erable body of work in sociolinguistics concerned with gender linguistic differ- ences. Experiments are performed on a large speech corpus of roughly 12000 con- versations. We …...

This paper presents an effective approach for resume information extraction to support automatic resume management and routing. A cascaded information extraction (IE) framework is designed. In the first pass, a resume is segmented into a consecutive blocks attached with labels indicating the information types. Then in the second pass, the …...

Information extraction systems incorpo- rate multiple stages of linguistic analysis. Although errors are typically compounded from stage to stage, it is possible to re- duce the errors in one stage by harnessing the results of the other stages. We dem- onstrate this by using the results of coreference analysis and …...

Entity relation detection is a form of in- formation extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from differ- ent levels of syntactic processing using kernel methods. Information from three different levels of processing is consid- ered: …...

Extracting semantic relationships between en- tities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based rela- tion extraction using SVM. Our study illus- trates that the base phrase chunking information is very effective for relation ex- traction and contributes to most of the …...

In this paper we present a supervised Word Sense Disambiguation methodol- ogy, that exploits kernel methods to model sense distinctions. In particular a combi- nation of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. We defined a ker- nel function, namely the Domain Kernel, that allowed …...

This paper presents a novel algorithm for the acquisition of Information Extraction patterns. The approach makes the assump- tion that useful patterns will have simi- lar meanings to those already identified as relevant. Patterns are compared using a variation of the standard vector space model in which information from an …...

Shortage of manually sense-tagged data is an obstacle to supervised word sense dis- ambiguation methods. In this paper we in- vestigate a label propagation based semi- supervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: simi- lar examples …...

We directly investigate a subject of much recent debate: do word sense disambiga- tion models help statistical machine trans- lation quality? We present empirical re- sults casting doubt on this common, but unproved, assumption. Using a state-of- the-art Chinese word sense disambigua- tion model to choose translation candi- dates for …...

The applicability of many current information ex- traction techniques is severely limited by the need for supervised training data. We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic ci- tations, small amounts of prior knowledge can be used to learn effective models in a …...