This research is focused on applying lexical semantics in parsing and PP attachment tasks.

Lexical semantics in parsing

Other notable examples of the successful incorporation of lexical semantics into parsing, not through word sense information but indirectly via selectional preferences, are Dowding et al.

This simple method allows us to incorporate lexical semantic information into the parser, without having to reimplement a full statistical parser.

The results are highly significant in demonstrating that a simplistic approach to incorporating lexical semantics into a parser significantly improves parser performance.

The fact that the improvement is larger for PP attachment than for full parsing is suggestive of PP attachment being a parsing subtask where lexical semantic information is particularly important, supporting the findings of Stetina and Nagao (1997) over a standalone PP attachment task.

Our hope is that this paper serves as the bridgehead for a new line of research into the impact of lexical semantics on parsing.

With any lexical semantic resource, we have to be careful to choose the appropriate level of granularity for a given task: if we limit ourselves to synsets we will not be able to capture broader gen-eralisations, such as the one between knife and scissors;1 on the other hand by grouping words related at a higher level in the hierarchy we could find that we make overly coarse groupings (e.g.

Our approach to exploring the impact of lexical semantics on parsing performance is to take two state-of-the-art statistical treebank parsers and pre-process the inputs variously.

Given our simple procedure for incorporating lexical semantics into the parsing process, our hope is that this research will open the door to further gains using more sophisticated parsing models and richer semantic options.

The performance gain obtained here is larger than in parsing, which is in accordance with the findings of Stetina and Nagao that lexical semantics has a considerable effect on PP attachment

The model cleanly incorporates both syntax and lexical semantics using quasi-synchronous dependency grammars (Smith and Eisner, 2006).

In this paper, we have presented a probabilistic model of paraphrase incorporating syntax, lexical semantics , and hidden loose alignments between two sentences’ trees.

We removed the lexical semantics component of the QG,10 and disallowed the syntactic configurations one by one, to investigate which components of mg contributes to system performance.

The lexical semantics component is critical, as seen by the drop in accuracy from the table (without this component, pQ behaves almost like the “all p” baseline).

Because dependency syntax is still only a crude approximation to semantic structure, we augment the model with a lexical semantics component, based on WordNet (Miller, 1995), that models how words are probabilistically altered in generating a paraphrase.

This combination of loose syntax and lexical semantics is similar to the “Jeopardy” model of Wang et al.

(2007) in treating the correspondences as latent variables, and in using a WordNet—based lexical semantics model to generate the target words.

5 We use log-linear models three times: for the configuration, the lexical semantics class, and the word.

WordNet relation(s) The model next chooses a lexical semantics relation between 3360-) and the yet-to-be-chosen word ti (line 12).

Unlike previous work, which primarily leverages syntactic analysis through dependency tree matching, we focus on improving the performance using models of lexical semantic resources.

Experiments show that our systems can be consistently and significantly improved with rich lexical semantic information, regardless of the choice of learning algorithms.

nent, lexical semantics .

We formulate answer selection as a semantic matching problem with a latent word-alignment structure as in (Chang et al., 2010) and conduct a series of experimental studies on leveraging recently proposed lexical semantic models.

First, by incorporating the abundant information from a variety of lexical semantic models, the answer selection system can be enhanced substantially, regardless of the choice of learning algorithms and settings.

1For example, Heilman and Smith (2010) emphasized that “The tree edit model, which does not use lexical semantics knowledge, produced the best result reported to date.”

In this work, we focus our study on leveraging the low-level semantic cues from recently proposed lexical semantic models.

Although lexical semantic information derived from WordNet has been used in some of these approaches, the research has mainly focused on modeling the mapping between the syntactic structures of questions and sentences, produced from syntactic analysis.

The potential improvement from enhanced lexical semantic models seems to have been deliberately overlooked.1

We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on Rhetorical Structure Theory.

We experimentally demonstrate that the discourse structure of non-factoid answers provides information that is complementary to lexical semantic similarity between question and answer, improving performance up to 24% (relative) over a state-of-the-art model that exploits lexical semantic similarity alone.

Lexical semantic features increase performance for all settings, but demonstrate far more utility to the open-domain YA corpus.

This disparity is likely due to the difficulty in assembling LS training data at an appropriate level for the biology corpus, contrasted with the relative abundance of large scale open-domain lexical semantic resources.

For the YA corpus, where lexical semantics showed the most benefit, simply adding

Lexical Semantics : We trained two different RNNLMs for this work.

We propose a novel answer reranking (AR) model that combines lexical semantics (LS) with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers and surface text information, and a deep one based on the Rhetorical Structure Theory (RST) discourse framework (Mann and Thompson, 1988).

4.3 Lexical Semantics Model

(2013), we include lexical semantics in our reranking model.

Inspired by this previous work and recent work in discourse parsing (Feng and Hirst, 2012), our work is the first to systematically explore structured discourse features driven by several discourse representations, combine discourse with lexical semantic models, and evaluate these representations on thousands of questions using both in-domain and cross-domain experiments.

Lexical semantic clue verifies whether a candidate term is related to the target product, and contextual semantic clue serves as a soft pattern miner to find candidates, which exploits semantics of each word in context so as to alleviate the data sparsity problem.

We build a semantic similarity graph to encode lexical semantic clue, and employ a convolutional neural model to capture contextual semantic clue.

LEX only uses lexical semantic clue.

Furthermore, LEX gets better recall than CONT and all syntax-based methods, which indicates that lexical semantic clue does aid to mine more infrequent features as expected.

We call it lexical semantic clue.

Then, based on the assumption that terms that are more semantically similar to the seeds are more likely to be product features, a graph which measures semantic similarities between terms is built to capture lexical semantic clue.

Then, a semantic similarity graph is created to capture lexical semantic clue, and a Convolutional Neural Network (CNN) (Collobert et al., 2011) is trained in each bootstrapping iteration to encode contextual semantic clue.

3.2 Capturing Lexical Semantic Clue in a Semantic Similarity Graph

To capture lexical semantic clue, each word is first converted into word embedding, which is a continuous vector with each dimension’s value corresponds to a semantic or grammatical interpretation (Turian et al., 2010).

In this paper, we propose to use as a parallel training dataset the definitions and glosses provided for the same term by different lexical semantic resources.

We compare monolingual translation models built from lexical semantic resources with two other kinds of datasets: manually-tagged question reformulations and question-answer pairs.

We have presented three datasets for training statistical word translation models for use in answer finding: question-answer pairs, manually-tagged question reformulations and glosses for the same term extracted from several lexical semantic resources.

question-answer pairs, and external knowledge, as contained in lexical semantic resources.

We use the definitions and glosses provided for the same term by different lexical semantic resources to automatically train the translation models.

This approach has been very recently made possible by the emergence of new kinds of lexical semantic and encyclopedic resources such as Wikipedia and Wiktionary.

3.2 Lexical Semantic Resources

Glosses and definitions for the same lexeme in different lexical semantic and encyclopedic resources can actually be considered as near-paraphrases, since they define the same terms and hence have

We henceforth propose a new approach for building monolingual translation models relying on domain-independent lexical semantic resources.

Knowledge-based measures rely on lexical semantic resources such as WordNet and comprise path length based measures (Rada et al., 1989) and concept vector based measures (Qiu and Frei, 1993).

4.1 Lexical Semantic Representation

The lexical semantic representation for strong and dog.

How humans represent meanings of individual words and how lexical semantic knowledge is combined to form complex concepts are issues fundamental to the study of human knowledge.

Given these early succesess in using fMRI to discriminate categorial information and to model lexical semantic representations of individual words, it is interesting to ask whether a similar approach can be used to study the representation of adjective-noun phrases.

In section 4, we discuss a vector-based approach to modeling the lexical semantic knowledge using word occurrence measures in a text corpus.

Lexical semantic factors, such as the semantic type of the clause predicate (5.c,e), or “well-established” kinds (5.g) may favour a generic reading, but such lexical factors are difficult to capture in a rule-based setting.

In the following, we will structure this feature space along two dimensions, distinguishing NP- and sentence-level factors as well as syntactic and semantic (including lexical semantic ) factors.

Semantic features include semantic features abstracted from syntax, such as tense and aspect or type of modification, but also lexical semantic features such as word sense classes, sense granularity or verbal predicates.

We proposed syntactic tree kernels enriched by lexical semantic similarity to tackle the portability of a relation extractor to different domains.

In the empirical evaluation on Automatic Content Extraction (ACE) data, we evaluate the impact of convolution tree kernels embedding lexical semantic similarities.

The same holds also for the lexical semantic kernel based on LSA (P_LSA), however, to only two out of three domains.

As the two semantically enriched kernels, PETLSA and PET_WC, seem to capture different information we use composite kernels (rows 10-11): the baseline kernel (PET) summed with the lexical semantic kernels.

We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation.

3.3 Lexical Semantic Similarity

We combine the alignment scores $001,, A8) with the scores ¢B(Ai, As) from the lexical semantic similarity measures into a single feature vector ¢(A,-,AS) = [¢G(A,-,AS)|¢B(A,-,AS)].

It builds on the development of lexical semantic resources and provides a platform for learning to utilize these resources.

As opposed to Roth and Frank, PARMA is designed as a a trainable platform for the incorporation of the sort of lexical semantic resources used in the related areas of Recognizing Textual Entailment (RTE) and Question Answering (QA).

The focus of PARMA is the integration of a diverse range of features based on existing lexical semantic resources.

While our results use geographical information in learning low-dimensional representations, other contextual variables are straightforward to include as well; incorporating effects for time — such as time of day, month of year and absolute year — may be a powerful tool for revealing periodic and historical influences on lexical semantics .

In this paper, we introduce a method that extends vector-space lexical semantic models to learn representations of geographically situated language.

Vector-space models of lexical semantics have been a popular and effective approach to learning representations of word meaning (Lin, 1998; Tumey and Pantel, 2010; Reisinger and Mooney, 2010; Socher et al., 2013; Mikolov et al., 2013, inter alia).

In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings.

Add to this that, beyond the standard lexical semantics challenges we tested here, predict models are currently been successfully applied in cutting-edge domains such as representing phrases (Mikolov et al., 2013c; Socher et al., 2012) or fusing language and vision in a common semantic space (Frome et al., 2013; Socher et al., 2013).

In this paper, we overcome the comparison scarcity problem by providing a direct evaluation of count and predict DSMs across many parameter settings and on a large variety of mostly standard lexical semantics benchmarks.

This work presents a set of experiments to investigate the use of lexical semantic information in dependency parsing of English.

Whether semantics improve parsing is one interesting research topic both on parsing and lexical semantics .

Broadly speaking, we can classify the methods to incorporate semantic information into parsers in two: systems using static lexical semantic repositories, such as WordNet or similar ontologies (Agirre et al., 2008; Agirre et al., 2011; Fujita et al., 2010), and systems using dynamic semantic clusters automatically acquired from corpora (Koo et al., 2008; Suzuki et al., 2009).

Therefore, we enrich each phrase node with features about its lexical pattern, its content information, and its lexical semantics:

3) Lexical Semantics .

If the node is a preterminal node, we capture its lexical semantic by adding features indicating its WordNet sense information.