Articles

Cube pruning is a fast inexact method for generating the items of a beam decoder. In this paper, we show that cube pruning is essentially equivalent to A* search on a specific search space with specific heuris- tics. We use this insight to develop faster and exact variants of cube …...

Minimum error rate training (MERT) in- volves choosing parameter values for a machine translation (MT) system that maximize performance on a tuning set as measured by an automatic evaluation met- ric, such as BLEU. The method is best when the system will eventually be eval- uated using the same metric, …...

Many statistical translation models can be regarded as weighted logical deduction. Under this paradigm, we use weights from the expectation semiring (Eisner, 2002), to compute first-order statistics (e.g., the ex- pected hypothesis length or feature counts) over packed forests of translations (lat- tices or hypergraphs). We then introduce a novel …...

We present an integrated dependency- based semantic role labeling system for English from both NomBank and Prop- Bank. By introducing assistant argument labels and considering much more fea- ture templates, two optimal feature tem- plate sets are obtained through an effec- tive feature selection procedure and help construct a high …...

Semantic Role Labeling (SRL) has proved to be a valuable tool for performing auto- matic analysis of natural language texts. Currently however, most systems rely on a large training set, which is manually an- notated, an effort that needs to be repeated whenever different languages or a differ- ent set …...

Unknown lexical items present a major obstacle to the development of broad- coverage semantic role labeling systems. We address this problem with a semi- supervised learning approach which ac- quires training instances for unseen verbs from an unlabeled corpus. Our method re- lies on the hypothesis that unknown lexical items …...

We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. The MAP semantic parse of a …...

In this paper, we propose a novel class of graphs, the tripartite directed acyclic graphs (tDAGs), to model first-order rule feature spaces for sentence pair classifi- cation. We introduce a novel algorithm for computing the similarity in first-order rewrite rule feature spaces. Our algorithm is extremely efficient and, as it …...

Methods that learn from prior informa- tion about input features such as general- ized expectation (GE) have been used to train accurate models with very little ef- fort. In this paper, we propose an ac- tive learning approach in which the ma- chine solicits “labels” on features rather than instances. …...

The problem of re-ranking initial retrieval re- sults exploring the intrinsic structure of docu- ments is widely researched in information re- trieval (IR) and has attracted a considerable amount of time and study. However, one of the drawbacks is that those algorithms treat queries and documents separately. Further- more, most …...

Automated mining of novel documents or sentences from chronologically ordered documents or sentences is an open chal- lenge in text mining. In this paper, we describe the preprocessing techniques for detecting novel Chinese text and discuss the influence of different Part of Speech (POS) filtering rules on the detection per- …...

In this paper, we propose a linear model-based general framework to combine k-best parse outputs from multiple parsers. The proposed framework leverages on the strengths of pre- vious system combination and re-ranking techniques in parsing by integrating them into a linear model. As a result, it is able to fully …...

This paper proposes a method that speeds up a classifier trained with many con- junctive features: combinations of (prim- itive) features. The key idea is to pre- compute as partial results the weights of primitive feature vectors that appear fre- quently in the target NLP task. A trie compactly stores …...

In this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions and relations between them. By taking advantage of the observation that a lot of product features are phrases, a con- cept of phrase …...

Set expansion refers to expanding a par- tial set of “seed” objects into a more com- plete set. One system that does set ex- pansion is SEAL (Set Expander for Any Language), which expands entities auto- matically by utilizing resources from the Web in a language-independent fashion. In this paper, …...

Named entity disambiguation concerns linking a potentially ambiguous mention of named entity in text to an unambigu- ous identifier in a standard database. One approach to this task is supervised classifi- cation. However, the availability of train- ing data is often limited, and the avail- able data sets tend to …...

Argumentative Zoning (AZ) is an anal- ysis of the argumentative and rhetorical structure of a scientific paper. It has been shown to be reliably used by independent human coders, and has proven useful for various information access tasks. Annota- tion experiments have however so far been restricted to one discipline, …...

In this paper we address the problem of identi- fying a broad range of term variations in Japa- nese web search queries, where these varia- tions pose a particularly thorny problem due to the multiple character types employed in its writing system. Our method extends the tech- niques proposed for …...

Most existing systems for Chinese Seman- tic Role Labeling (SRL) make use of full syntactic parses. In this paper, we evalu- ate SRL methods that take partial parses as inputs. We first extend the study on Chi- nese shallow parsing presented in (Chen et al., 2006) by raising a set …...

It is well known that pragmatic knowl- edge is useful and necessary in many dif- ficult language processing tasks, but be- cause this knowledge is difficult to acquire and process automatically, it is rarely used. We present an open information extrac- tion technique for automatically extracting a particular kind of …...