Articles

This paper introduces a new parser eval- uation corpus containing around 700 sen- tences annotated with unbounded depen- dencies, from seven different grammatical constructions. We run a series of off-the- shelf parsers on the corpus to evaluate how well state-of-the-art parsing technology is able to recover such dependencies. The overall …...

Bilingual dictionaries are vital resources in many areas of natural language processing. Numerous methods of machine translation re- quire bilingual dictionaries with large cover- age, but less-frequent language pairs rarely have any digitalized resources. Since the need for these resources is increasing, but the hu- man resources are scarce for …...

In the early days of email, widely-used conventions for indicating quoted reply content and email signatures made it easy to segment email messages into their func- tional parts. Today, the explosion of dif- ferent email formats and styles, coupled with the ad hoc ways in which people vary the structure …...

This paper presents a supervised method for resolving metonymies. We enhance a commonly used feature set with fea- tures extracted based on collocation in- formation from corpora, generalized us- ing lexical and encyclopedic knowledge to determine the preferred sense of the potentially metonymic word using meth- ods from unsupervised word …...

Sets of lexical items sharing a significant aspect of their meaning (concepts) are fun- damental in linguistics and NLP. Manual concept compilation is labor intensive, er- ror prone and subjective. We present a web-based concept extension algorithm. Given a set of terms specifying a concept in some language, we translate …...

Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychol- ogy. Accurate measurement of semantic similarity between words is essential for various tasks such as, document cluster- ing, information retrieval, and synonym extraction. We propose a novel model …...

Measuring the similarity between two texts is a fundamental problem in many NLP and IR applications. Among the ex- isting approaches, the cosine measure of the term vectors representing the origi- nal texts has been widely used, where the score of each term is often determined by a TFIDF formula. …...

In this paper, we first compare several strategies to handle the newly proposed three-way Recognizing Textual Entailment (RTE) task. Then we define a new mea- surement for a pair of texts, called Textual Relatedness, which is a weaker concept than semantic similarity or paraphrase. We show that an alignment model …...

Strictly corpus-based measures of seman- tic distance conflate co-occurrence infor- mation pertaining to the many possible senses of target words. We propose a corpus–thesaurus hybrid method that uses soft constraints to generate word-sense- aware distributional profiles (DPs) from coarser “concept DPs” (derived from a Roget-like thesaurus) and sense-unaware traditional word …...

Randomised techniques allow very big language models to be represented suc- cinctly. However, being batch-based they are unsuitable for modelling an un- bounded stream of language whilst main- taining a constant error rate. We present a novel randomised language model which uses an online perfect hash function to efficiently deal …...

While speaking spontaneously, speakers often make errors such as self-correction or false starts which interfere with the successful application of natural language processing techniques like summarization and machine translation to this data. There is active work on reconstructing this error- ful data into a clean and fluent transcript by identifying …...

The recent availability of large corpora for training N-gram language models has shown the utility of models of higher or- der than just trigrams. In this paper, we investigate methods to control the increase in model size resulting from applying stan- dard methods at higher orders. We in- troduce significance-based …...

This paper describes a time-series model for parsing transcribed speech containing disfluencies. This model differs from pre- vious parsers in its explicit modeling of a buffer of recent words, which allows it to recognize repairs more easily due to the frequent overlap in words between errors and their repairs. The …...

Tree Adjoining Grammars have well-known advantages, but are typically considered too difficult for practical systems. We demon- strate that, when done right, adjoining im- proves translation quality without becoming computationally intractable. Using adjoining to model optionality allows general translation patterns to be learned without the clutter of endless variations of …...

Training a statistical machine translation starts with tokenizing a parallel corpus. Some languages such as Chinese do not in- corporate spacing in their writing system, which creates a challenge for tokenization. Moreover, morphologically rich languages such as Korean present an even bigger challenge, since optimal token boundaries for machine translation …...

Current statistical machine translation (SMT) systems are trained on sentence- aligned and word-aligned parallel text col- lected from various sources. Translation model parameters are estimated from the word alignments, and the quality of the translations on a given test set depends on the parameter estimates. There are at least two …...

Structured syntactic knowledge is important for phrase reordering. This paper proposes us- ing convolution tree kernel over source parse tree to model structured syntactic knowledge for BTG-based phrase reordering in the con- text of statistical machine translation. Our study reveals that the structured syntactic fea- tures over the source phrases …...

We propose a new model for unsupervised POS tagging based on linguistic distinc- tions between open and closed-class items. Exploiting notions from current linguis- tic theory, the system uses far less infor- mation than previous systems, far simpler computational methods, and far sparser descriptions in learning contexts. By ap- plying …...

Many approaches to unsupervised mor- phology acquisition incorporate the fre- quency of character sequences with re- spect to each other to identify word stems and affixes. This typically involves heuris- tic search procedures and calibrating mul- tiple arbitrary thresholds. We present a simple approach that uses no thresholds other than …...

Inspired by the success of English grapheme-to-phoneme research in speech synthesis, many researchers have pro- posed phoneme-based English-to-Chinese transliteration models. However, such ap- proaches have severely suffered from the errors in Chinese phoneme-to-grapheme conversion. To address this issue, we propose a new English-to-Chinese transliteration model and make system- atic comparisons …...