Articles

We present a classifier to predict con- textual polarity of subjective phrases in a sentence. Our approach features lexi- cal scoring derived from the Dictionary of Affect in Language (DAL) and extended through WordNet, allowing us to automat- ically score the vast majority of words in our input avoiding the …...

This paper present an overview of some emerging trends in the application of NLP in the domain of the so-called Digital Hu- manities and discusses the role and nature of metadata, the annotation layer that is so characteristic of documents that play a role in the scholarly practises of the …...

We present a simple and effective method for extracting parallel sentences from comparable corpora. We employ a sta- tistical machine translation (SMT) system built from small amounts of parallel texts to translate the source side of the non- parallel corpus. The target side texts are used, along with other corpora, …...

This paper discusses computational com- positional semantics from the perspective of grammar engineering, in the light of ex- perience with the use of Minimal Recur- sion Semantics in DELPH-IN grammars. The relationship between argument index- ation and semantic role labelling is ex- plored and a semantic dependency nota- tion (DMRS) …...

We propose a new graph-based semi- supervised learning (SSL) algorithm and demonstrate its application to document categorization. Each document is represented by a vertex within a weighted undirected graph and our proposed framework minimizes the weighted Kullback-Leibler divergence between distributions that encode the class membership probabilities of each vertex. The …...

String-to-string transduction is a central prob- lem in computational linguistics and natural language processing. It occurs in tasks as di- verse as name transliteration, spelling correc- tion, pronunciation modeling and inflectional morphology. We present a conditional log- linear model for string-to-string transduction, which employs overlapping features over la- tent alignment …...

Dictionary-based translation is a traditional approach in use by cross-language information retrieval systems. However, significant performance degradation is often observed when queries contain words that do not appear in the dictionary. This is called the Out of Vocabulary (OOV) problem. In recent years, Web mining has been shown to be …...

Many Web news portals have provided clustered news categories for readers to browse many related news articles. However, to the best of our knowledge, they only provide monolingual services. For readers who want to find related news articles in different languages, the search process is very cumbersome. In this paper, …...

Unknown term translation is important to CLIR and MT systems, but it is still an unsolved problem. Recently, a few researchers have proposed several effective search-result-based term translation extraction methods which explore search results to discover translations of frequent unknown terms from Web search results. However, many infrequent unknown terms, …...

One major problem in multilingual Question Answering (QA) is the integration of information obtained from different languages into one single ranked list. This paper proposes two different architectures to overcome this problem. The first one performs the information merging at passage level, whereas the second does it at answer level. …...

Experiments carried out within evaluation initiatives for information retrieval have been building a substantial resource for further detailed research. In this study, we present a comprehensive analysis of the data of the Cross Language Evaluation Forum (CLEF) from the years 2000 to 2004. Features of the topics are related to …...

This paper describes the design and evaluation of an extractive summarizer for educational science content called COGENT. COGENT extends MEAD based on strategies elicited from an em- pirical study with science domain and in- structional design experts. COGENT identifies sentences containing pedagogi- cally relevant concepts for a specific sci- ence …...

In this paper, we present an approach to the automatic identification and correction of preposition and determiner errors in non- native (L2) English writing. We show that models of use for these parts of speech can be learned with an accuracy of 70.06% and 92.15% respectively on L1 text, and …...

This paper presents a new fully auto- matic method for building highly dense and accurate knowledge bases from ex- isting semantic resources. Basically, the method uses a wide-coverage and accu- rate knowledge-based Word Sense Dis- ambiguation algorithm to assign the most appropriate senses to large sets of topi- cally related …...

This paper presents an implemented hy- brid approach to grammar and style checking, combining an industrial pattern- based grammar and style checker with bi- directional, large-scale HPSG grammars for German and English. Under this ap- proach, deep processing is applied selec- tively based on the error hypotheses of a shallow …...

In this paper, we analyze the state of cur- rent human and automatic evaluation of topic-focused summarization in the Docu- ment Understanding Conference main task for 2005-2007. The analyses show that while ROUGE has very strong correlation with responsiveness for both human and automatic summaries, there is a signifi- cant …...

In this paper we generalise the sen- tence compression task. Rather than sim- ply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. We present a new corpus that is suited to our task and …...

This paper proposes an other-anaphora resolution approach in bio-medical texts. It utilizes automatically mined patterns to discover the semantic relation between an anaphor and a candidate antecedent. The knowledge from lexical patterns is incor- porated in a machine learning framework to perform anaphora resolution. The ex- periments show that machine …...

We describe an entirely statistics-based, unsupervised, and language- independent approach to multilingual information retrieval, which we call La- tent Morpho-Semantic Analysis (LMSA). LMSA overcomes some of the shortcomings of related previous ap- proaches such as Latent Semantic Analysis (LSA). LMSA has an impor- tant theoretical advantage over LSA: it combines …...

In this paper, we focus on the adaptation problem that has a large labeled data in the source domain and a large but unlabeled data in the target domain. Our aim is to learn reliable information from unlabeled target domain data for dependency pars- ing adaptation. Current state-of-the-art sta- tistical …...