Articles

In this paper, we discuss how to utilize the co-occurrence of answers in building an automatic question answering system that answers a series of questions on a specific topic in a batch mode. Experi- ments show that the answers to the many of the questions in the series usually have …...

In this paper, we propose a sentence ordering algorithm using a semi-supervised sentence classification and historical ordering strategy. The classification is based on the manifold structure underlying sentences, addressing the problem of limited labeled data. The historical ordering helps to ensure topic continuity and avoid topic bias. Experiments demonstrate that …...

In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations generated for a given input se- mantics. Three models (and several vari- ants) are trained and tested: an n-gram language model, a discriminative maxi- mum …...

We present an unsupervised learning ap- proach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. It works by calculating eigen- vectors of an adjacency graph’s Lapla- cian to recover a submanifold of data from a high dimensionality space and then …...

When a machine learning-based named entity recognition system is employed in a new domain, its performance usually de- grades. In this paper, we provide an em- pirical study on the impact of training data size and domain information on the per- formance stability of named entity recog- nition models. We …...

Co-occurrence analysis has been used to determine related words or terms in many NLP-related applications such as query expansion in Information Retrieval (IR). However, related words are usually determined with respect to a single word, without relevant information for its application context. For example, the word “programming” may be considered …...

We present an investigation of recently proposed character and word sequence kernels for the task of authorship attribu- tion based on relatively short texts. Per- formance is compared with two corre- sponding probabilistic approaches based on Markov chains. Several configurations of the sequence kernels are studied on a relatively large …...

Entity annotation involves attaching a la- bel such as ‘name’ or ‘organization’ to a sequence of tokens in a document. All the current rule-based and machine learning- based approaches for this task operate at the document level. We present a new and generic approach to entity annotation which uses the …...

Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many …...

Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmenta- tion and labeling. Both models have ad- vantages in terms of the type of features they most naturally represent. We pro- pose a hybrid model that is capable of rep- resenting both types of …...

We extended language modeling ap- proaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our ap- proach is based on the analogy between IR and CF, especially between CF and rel- evance feedback (RF). Both CF and RF exploit users’ preference/relevance judg- ments to recommend …...

Information Extraction (IE) is the task of extracting knowledge from unstructured text. We present a novel unsupervised approach for information extraction based on graph mutual reinforcement. The proposed approach does not require any seed patterns or examples. Instead, it depends on redundancy in large data sets and graph based mutual …...

Random Indexing is a vector space tech- nique that provides an efficient and scal- able approximation to distributional simi- larity problems. We present experiments showing Random Indexing to be poor at handling large volumes of data and evalu- ate the use of weighting functions for im- proving the performance of …...

Lexical features are key to many ap- proaches to sentiment analysis and opin- ion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico- syntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their …...

We present an approach for the joint ex- traction of entities and relations in the con- text of opinion recognition and analysis. We identify two types of opinion-related entities — expressions of opinions and sources of opinions — along with the link- ing relation that exists between them. In- spired …...

User-supplied reviews are widely and increasingly used to enhance e- commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manu- ally, in this paper we consider the task of automatically …...

This paper explores the use of a character segment based character correction model, language modeling, and shallow morphology for Arabic OCR error cor- rection. Experimentation shows that character segment based correction is su- perior to single character correction and that language modeling boosts correction, by improving the ranking of candidate …...

Adapting language models across styles and topics, such as for lecture transcrip- tion, involves combining generic style models with topic-specific content rele- vant to the target document. In this work, we investigate the use of the Hid- den Markov Model with Latent Dirichlet Allocation (HMM-LDA) to obtain syn- tactic state …...

We investigate the problem of learn- ing a part-of-speech (POS) lexicon for a resource-poor language, dialectal Arabic. Developing a high-quality lexicon is often the first step towards building a POS tag- ger, which is in turn the front-end to many NLP systems. We frame the lexicon ac- quisition problem as …...

This paper presents a corrective model for speech recognition of inflected lan- guages. The model, based on a discrim- inative framework, incorporates word n- grams features as well as factored mor- phological features, providing error reduc- tion over the model based solely on word n-gram features. Experiments on a large …...