Articles

We consider a parsed text corpus as an in- stance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations be- tween them. We show that graph walks, com- bined with existing techniques of supervised learning, can be used to derive a task-specific …...

We address the task of computing vector space representations for the meaning of word oc- currences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing mod- els for this task do not take …...

Statistical parsers have become increasingly accurate, to the point where they are useful in many natural language applications. However, estimating parsing accuracy on a wide variety of domains and genres is still a challenge in the absence of gold-standard parse trees. In this paper, we propose a technique that au- …...

We show that jointly parsing a bitext can sub- stantially improve parse quality on both sides. In a maximum entropy bitext parsing model, we define a distribution over source trees, tar- get trees, and node-to-node alignments be- tween them. Features include monolingual parse scores and various measures of syntac- tic …...

We present a discriminative, latent variable approach to syntactic parsing in which rules exist at multiple scales of refinement. The model is formally a latent variable CRF gram- mar over trees, learned by iteratively splitting grammar productions (not categories). Dif- ferent regions of the grammar are refined to different degrees, …...

Traditionally, statistical machine translation systems have relied on parallel bi-lingual data to train a translation model. While bi-lingual parallel data are expensive to generate, mono- lingual data are relatively common. Yet mono- lingual data have been under-utilized, having been used primarily for training a language model in the target language. …...

While phrase-based statistical machine trans- lation systems currently deliver state-of-the- art performance, they remain weak on word order changes. Current phrase reordering models can properly handle swaps between adjacent phrases, but they typically lack the ability to perform the kind of long-distance re- orderings possible with syntax-based systems. In this …...

Confusion networks are a simple representa- tion of multiple speech recognition or transla- tion hypotheses in a machine translation sys- tem. A typical operation on a confusion net- work is to find the path which minimizes or maximizes a certain evaluation metric. In this article, we show that this problem …...

In domains with insufficient matched training data, language models are often constructed by interpolating component models trained from partially matched corpora. Since the n- grams from such corpora may not be of equal relevance to the target domain, we propose an n-gram weighting technique to adjust the component n-gram probabilities …...

To improve the Mandarin large vocabulary continuous speech recognition (LVCSR), a unified framework based approach is intro- duced to exploit multi-level linguistic knowl- edge. In this framework, each knowledge source is represented by a Weighted Finite State Transducer (WFST), and then they are combined to obtain a so-called analyzer for …...

We introduce a method for solving substi- tution ciphers using low-order letter n-gram models. This method enforces global con- straints using integer programming, and it guarantees that no decipherment key is over- looked. We carry out extensive empirical ex- periments showing how decipherment accu- racy varies as a function of …...

The alignment problem—establishing links between corresponding phrases in two related sentences—is as important in natural language inference (NLI) as it is in machine transla- tion (MT). But the tools and techniques of MT alignment do not readily transfer to NLI, where one cannot assume semantic equiva- lence, and for which …...

Determining the polarity of a sentiment- bearing expression requires more than a sim- ple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particu- lar overall polarity. In this paper, we view such subsentential interactions in light of composi- tional semantics, …...

In this paper we describe research on sum- marizing conversations in the meetings and emails domains. We introduce a conver- sation summarization system that works in multiple domains utilizing general conversa- tional features, and compare our results with domain-dependent systems for meeting and email data. We find that by treating …...

Information of interest to users is often dis- tributed over a set of documents. Users can specify their request for information as a query/topic – a set of one or more sentences or questions. Producing a good summary of the relevant information relies on understand- ing the query and linking …...

Minimum Error Rate Training (MERT) is an effective means to estimate the feature func- tion weights of a linear model such that an automated evaluation criterion for measuring system performance can directly be optimized in training. To accomplish this, the training procedure determines for each feature func- tion its exact …...

The graph-based ranking algorithm has been recently exploited for multi-document sum- marization by making only use of the sen- tence-to-sentence relationships in the documents, under the assumption that all the sentences are indistinguishable. However, given a document set to be summarized, dif- ferent documents are usually not equally im- portant, …...

The performance of machine translation sys- tems varies greatly depending on the source and target languages involved. Determining the contribution of different characteristics of language pairs on system performance is key to knowing what aspects of machine transla- tion to improve and which are irrelevant. This paper investigates the effect …...

An important problem in translation neglected by most recent statistical machine translation systems is insertion and deletion of words, such as function words, motivated by linguistic structure rather than adjacent lexical context. Phrasal and hierarchical systems can only insert or delete words in the context of a larger phrase or …...

In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signals, and another that finds the full scope of these nega- tion signals. Our …...