Articles

This paper describes the PKU_HIT system on event detection in the SemEval-2010 Task. We construct three modules for the three sub-tasks of this evaluation. For target verb WSD, we build a Naïve Bayesian classifier which uses additional training instances expanded from an untagged Chinese corpus automatically. For sentence SRL and …...

The CL Research system for SemEval-2 Task 10 for linking events and their participants in discourse is an exploration of the use of a spe- cially created FrameNet dictionary that cap- tures all FrameNet information about frames, lexical units, and frame-to-frame relations. This system is embedded in a specially de- …...

This paper describes our system participating in task 18 of SemEval-2010, i.e. disambiguating Sentiment- Ambiguous Adjectives (SAAs). To disambiguating SAAs, we compare the machine learning-based and lexicon- based methods in our submissions: 1) Maximum entropy is used to train classifiers based on the annotated Chinese data from the NTCIR opinion …...

This paper presents TIPSem, a system to extract temporal information from natural language texts for English and Spanish. TIPSem, learns CRF models from training data. Although the used features include different language analysis levels, the ap- proach is focused on semantic informa- tion. For Spanish, TIPSem achieved the best F1 …...

Extracting temporal information from raw text is fundamental for deep language understand- ing, and key to many applications like ques- tion answering, information extraction, and document summarization. In this paper, we describe two systems we submitted to the TempEval 2 challenge, for extracting temporal information from raw text. The systems …...

inThis paper describes our participation the the SemEval-2010 Task #12, Parser Evaluation using Textual Entail- ment. Our system incorporated two depen- dency parsers, one semantic role labeler, and a deep parser based on hand-crafted grammars. The shortest path algorithm is applied on the graph representation of the parser outputs. Then, …...

The system to spot INIs, DNIs and their anteced- ents is an adaptation of VENSES, a system for semantic evaluation that has been used for RTE challenges in the last 6 years. In the following we will briefly describe the system and then the ad- ditions we made to cope …...

This paper describes the Cambridge sub- mission to the SemEval-2010 Parser Eval- uation using Textual Entailment (PETE) task. We used a simple definition of en- tailment, parsing both T and H with the C&C parser and checking whether the core grammatical relations (subject and object) produced for H were a …...

In this paper we describe Mephisto, our system for Task 9 of the SemEval-2 work- shop. Our approach to this task is to de- velop a machine learning classifier which determines for each verb pair describing a noun compound which verb should be ranked higher. These classifications are then combined …...

This paper describes the SEMAFOR sys- tem’s performance in the SemEval 2010 task on linking events and their partici- pants in discourse. Our entry is based upon SEMAFOR 1.0 (Das et al., 2010a), a frame-semantic probabilistic parser built from log-linear models. The extended sys- tem models null instantiations, including non-local …...

This paper describes our system for SemEval-2010 Task 8 on multi-way clas- sification of semantic relations between nominals. First, the type of semantic re- lation is classified. Then a relation type- specific classifier determines the relation direction. Classification is performed us- ing SVM classifiers and a number of fea- tures …...

This paper describes our system for the classification of argument coercion for SemEval-2010 Task 7. We present two ap- proaches to classifying an argument’s se- mantic class, which is then compared to the predicate’s expected semantic class to detect coercions. The first approach is based on learning the members of …...

This paper presents the participation of the University of Bari (UBA) at the SemEval- 2010 Cross-Lingual Lexical Substitution Task. The goal of the task is to substi- tute a word in a language Ls, which oc- curs in a particular context, by provid- ing the best synonyms in a different …...

We describe a system which ranks human- provided paraphrases of noun compounds, where the frequency with which a given paraphrase was provided by human volun- teers is the gold standard for ranking. Our system assigns a score to a paraphrase of a given compound according to the num- ber of …...

This paper describes our approach to the automatic identification of semantic rela- tions between nominals in English sen- tences. The basic idea of our strategy is to develop machine-learning classifiers which: (1) make use of class-independent features and classifier; (2) make use of a simple and effective feature set without …...

This paper addresses the problem of rank- ing a list of paraphrases associated with a noun-noun compound as closely as possi- ble to human raters (Butnariu et al., 2010). UCD-Goggle tackles this task using se- mantic knowledge learnt from the Google n-grams together with human-preferences for paraphrases mined from training …...

The automatic interpretation of semantic relations between nominals is an impor- tant subproblem within natural language understanding applications and is an area of increasing interest. In this paper, we present the system we used to participate in the SEMEVAL 2010 Task 8 Multi-Way Classification of Semantic Relations be- tween Pairs …...

We describe a boosting-based supervised learning approach to the “Multi-Way Clas- sification of Semantic Relations between Pairs of Nominals” task #8 of SemEval- 2. Participants were asked to determine which relation, from a set of nine relations plus “Other”, exists between two nomi- nals, and also to determine the roles …...

We present an approach for semantic re- lation extraction between nominals that combines semantic information with shal- low syntactic processing. We propose to use the ResearchCyc knowledge base as a source of semantic information about nominals. Each source of information is represented by a specific kernel func- tion. The experiments …...

This paper describes the Cross-Lingual Word Sense Disambiguation system UvT- WSD1, developed at Tilburg University, for participation in two SemEval-2 tasks: the Cross-Lingual Word Sense Disam- biguation task and the Cross-Lingual Lex- ical Substitution task. The UvT-WSD1 system makes use of k-nearest neighbour classifiers, in the form of single-word ex- …...