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The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.

This algorithm can be applied to statistical machine translation.

Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.

Floating point values may be stored in the trie exactly, using 31 bits for non-positive log probability and 32 bits for backoff5.

The approach has been successfully tested on the 8 000-word Verbmobil task.

Only one of the first n positions which are not already aligned in a partial hypothesis may be chosen, where n is set to 4.

The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.

The code is opensource, has minimal dependencies, and offers both C++ and Java interfaces for integration.

The AdaBoost algorithm was developed for supervised learning.

(Blum and Mitchell 98) offer a promising formulation of redundancy, also prove some results about how the use of unlabeled examples can help classification, and suggest an objective function when training with unlabeled examples.

Foster et all describe a new approach to SMT adaptation that weights out-of-domain phrase pairs according to their relevance to the target domain, determined by both how similar to it they appear to be, and whether they belong to general language or not.

Recent work by Finkel and Manning (2009) which re-casts Daum´e’s approach in a hierarchical MAP framework may be applicable to this problem.

The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.

This is not completely surprising, since all systems use very similar technology.

The second algorithm builds on a boosting algorithm called AdaBoost.

The weak hypothesis chosen was then restricted to be a predictor in favor of this label.

Their results suggested that it was possible to learn accurate POS taggers for languages which did not have any annotated data, but have translations into a resource-rich language.

While there might be some controversy about the exact definition of such a tagset, these 12 categories cover the most frequent part-of-speech and exist in one form or another in all of the languages that we studied.

They have made use of local and global features to deal with the instances of same token in a document.

The system described in this paper is similar to the MENE system of (Borthwick, 1999).

It is well-known that English constituency parsing models do not generalize to other languages and treebanks.

Also surprising is the low test set OOV rate given the possibility of morphological variation in Arabic.

The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.

So, who won the competition?

This topic has been getting more attention, driven by the needs of various NLP applications.

Extract NE pair instances with contexts From the four years of newspaper corpus, we extracted 1.9 million pairs of NE instances.

Replacing this with a ranked evaluation seems to be more suitable.

One annotator suggested that this was the case for as much as 10% of our test sentences.

The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.

However, when we pre- tag the input—as is recommended for English— we notice a 0.57% F1 improvement.

they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement.

12 One class of full personal names that this characterization does not cover are married women's names.

they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.

nan2gual 'pumpkin.'

The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.

Another approach to finding paraphrases is to find phrases which take similar subjects and objects in large corpora by using mutual information of word distribution [Lin and Pantel 01].

They focused on phrases which two Named Entities, and proceed in two stages.

So, we set a threshold that at least two examples are required to build a link.

While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems.

It was also proposed to allow annotators to skip sentences that they are unable to judge.

These clusters are computed using an SVD variant without relying on transitional structure.

The state-to-tag mapping is obtained from the best hyperparameter setting for 11 mapping shown in Table 3.

This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.

Our baseline for all sentence lengths is 5.23% F1 higher than the best previous result.

This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration.

Word type N % Dic tion ary entr ies 2 , 5 4 3 9 7 . 4 7 Mor pho logi call y deri ved wor ds 3 0 . 1 1 Fore ign tran slite rati ons 9 0 . 3 4 Per son al na mes 5 4 2 . 0 7 cases.

Using a treebank grammar, the data-driven lexicon, and a linguistically motivated unknown-tokens handling technique their model outperformed previous pipelined, integrated or factorized systems for Hebrew morphological and syntactic processing, yielding an error reduction of 12% over the best published results so far.

The Grammar Our parser looks for the most likely tree spanning a single path through the lattice of which the yield is a sequence of lexemes.

The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.

(f1; ;mg ; l) 2 (f1; ;mg n fl; l1g ; l0) !

On the basis of that observation, they described a class of formalisms which they called Linear Context- Free Rewriting Systems (LCFRs), and showed they were recognizable in polynomial time and generated only semilinear languages.

If 7 is an elementary tree, the derivation tree consists of a single node labeled 7.

They showed better grammars to improve performance on both morphological and syntactic tasks, providing support for the advantage of a joint framework over pipelined or factorized ones.

The current work treats both segmental and super-segmental phenomena, yet we note that there may be more adequate ways to treat supersegmental phenomena assuming Word-Based morphology as we explore in (Tsarfaty and Goldberg, 2008).

Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.

This design leads to a significant reduction in the computational complexity of training and inference.

Vijay-Shankar et all considered the structural descriptions produced by various grammatical formalisms in terms of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate.

This property reflects an important aspect of the underlying linguistic theory associated with the formalism.

In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.

Since the segmentation corresponds to the sequence of words that has the lowest summed unigram cost, the segmenter under discussion here is a zeroth-order model.

Using a treebank grammar, the data-driven lexicon, and a linguistically motivated unknown-tokens handling technique their model outperformed previous pipelined, integrated or factorized systems for Hebrew morphological and syntactic processing, yielding an error reduction of 12% over the best published results so far.

Tsarfaty and Sima’an (2007) have reported state-of-the-art results on Hebrew unlexicalized parsing (74.41%) albeit assuming oracle morphological segmentation.

The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.

The maximum entropy framework estimates probabilities based on the principle of making as few assumptions as possible, other than the constraints imposed.

The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.

The other half was replaced by other participants, so we ended up with roughly the same number.

Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs.

We generate these caseframes automatically by running AutoSlog over the training corpus exhaustively so that it literally generates a pattern to extract every noun phrase in the corpus.

The approach has been successfully tested on the 8 000-word Verbmobil task.

6.

Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints.

Our second point of comparison is with Grac¸a et al.

This paper talks about KenLM: Faster and Smaller Language Model Queries.

Then we ran binary search to determine the least amount of memory with which it would run.

The overall performance of their joint framework demonstrated that a probability distribution obtained over mere syntactic contexts using a Treebank grammar and a data-driven lexicon outperformed upper bounds proposed by previous joint disambiguation systems and achieved segmentation and parsing results on a par with state-of-the-art standalone applications results.

Using a wide-coverage morphological analyzer based on (Itai et al., 2006) should cater for a better coverage, and incorporating lexical probabilities learned from a big (unannotated) corpus (cf.

they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.

gaolbu4-gaolxing4 (hap-not-happy) 'happy?'

Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.

Rather we believe several methods have to be developed using different heuristics to discover wider variety of paraphrases.

In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures.

A graph D = (W, A) is well-formed iff it is acyclic and connected.

This paper talks about Pseudo-Projective Dependency Parsing.

The dependency graph in Figure 1 satisfies all the defining conditions above, but it fails to satisfy the condition ofprojectivity (Kahane et al., 1998): The arc connecting the head jedna (one) to the dependent Z (out-of) spans the token je (is), which is not dominated by jedna.

The approach has been successfully tested on the 8 000-word Verbmobil task.

Kollege.

In this paper, Ben and Riloff present a coreference resolver called BABAR that focuses on the use of contextual-role knowledge for coreference resolution.

Section 2.1 describes how BABAR generates training examples to use in the learning process.

Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.

There is a ‘core corpus’ of ten commentaries, for which the range of information (except for syntax) has been completed; the remaining data has been annotated to different degrees, as explained below.

BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results.

3 These are not full case frames in the traditional sense, but they approximate a simple case frame with a single slot.

The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.

In fact, we found that enabling IRSTLM’s cache made it slightly slower, so results in Table 1 use IRSTLM without caching.

Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.

The significant drop in number of pupils will begin in the fall of 2003.

Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints.

1 53.8 47.

Using less training data than other systems, their NER can perform as well as other state-of-the-art NERs.

By using the output of a hand-coded system such as Proteus, MENE can improve its performance, and can even outperform IdentiFinder (Borthwick, 1999).

Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.

The 1-bit sign is almost always negative and the 8-bit exponent is not fully used on the range of values, so in practice this corresponds to quantization ranging from 17 to 20 total bits.

Das and Petrov, in this paper, approached inducing unsupervised part-of-speech taggers for languages that had no labeled training data, but had translated text in a resource-rich language.

Note that because we extracted only high-confidence alignments, many foreign vertices will not be connected to any English vertices.

Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.

The text type are editorials instead of speech transcripts.

Using a treebank grammar, the data-driven lexicon, and a linguistically motivated unknown-tokens handling technique their model outperformed previous pipelined, integrated or factorized systems for Hebrew morphological and syntactic processing, yielding an error reduction of 12% over the best published results so far.

In the current work morphological analyses and lexical probabilities are derived from a small Treebank, which is by no means the best way to go.

Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.

There are still some open issues to be resolved with the format, but it represents a first step.

The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.

Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-7) (1-8) (1-6) lcc (1-6) (1-7) (1-4) utd (1-7) (1-6) (2-7) upc-mr (1-8) (1-6) (1-7) nrc (1-7) (2-6) (8) ntt (1-8) (2-8) (1-7) cmu (3-7) (4-8) (2-7) rali (5-8) (3-9) (3-7) systran (9) (8-9) (10) upv (10) (10) (9) Spanish-English (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-7) (1-6) (1-5) ntt (1-7) (1-8) (1-5) lcc (1-8) (2-8) (1-4) utd (1-8) (2-7) (1-5) nrc (2-8) (1-9) (6) upc-mr (1-8) (1-6) (7) uedin-birch (1-8) (2-10) (8) rali (3-9) (3-9) (2-5) upc-jg (7-9) (6-9) (9) upv (10) (9-10) (10) German-English (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) uedin-phi (1-2) (1) (1) lcc (2-7) (2-7) (2) nrc (2-7) (2-6) (5-7) utd (3-7) (2-8) (3-4) ntt (2-9) (2-8) (3-4) upc-mr (3-9) (6-9) (8) rali (4-9) (3-9) (5-7) upc-jmc (2-9) (3-9) (5-7) systran (3-9) (3-9) (10) upv (10) (10) (9) Figure 7: Evaluation of translation to English on in-domain test data 112 English-French (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) nrc (1-5) (1-5) (1-6) upc-mr (1-4) (1-5) (1-6) upc-jmc (1-6) (1-6) (1-5) systran (2-7) (1-6) (7) utd (3-7) (3-7) (3-6) rali (1-7) (2-7) (1-6) ntt (4-7) (4-7) (1-5) English-Spanish (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) ms (1-5) (1-7) (7-8) upc-mr (1-4) (1-5) (1-4) utd (1-5) (1-6) (1-4) nrc (2-7) (1-6) (5-6) ntt (3-7) (1-6) (1-4) upc-jmc (2-7) (2-7) (1-4) rali (5-8) (6-8) (5-6) uedin-birch (6-9) (6-10) (7-8) upc-jg (9) (8-10) (9) upv (9-10) (8-10) (10) English-German (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-mr (1-3) (1-5) (3-5) ntt (1-5) (2-6) (1-3) upc-jmc (1-5) (1-4) (1-3) nrc (2-4) (1-5) (4-5) rali (3-6) (2-6) (1-4) systran (5-6) (3-6) (7) upv (7) (7) (6) Figure 8: Evaluation of translation from English on in-domain test data 113 French-English (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-5) (1-8) (1-4) cmu (1-8) (1-9) (4-7) systran (1-8) (1-7) (9) lcc (1-9) (1-9) (1-5) upc-mr (2-8) (1-7) (1-3) utd (1-9) (1-8) (3-7) ntt (3-9) (1-9) (3-7) nrc (3-8) (3-9) (3-7) rali (4-9) (5-9) (8) upv (10) (10) (10) Spanish-English (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-2) (1-6) (1-3) uedin-birch (1-7) (1-6) (5-8) nrc (2-8) (1-8) (5-7) ntt (2-7) (2-6) (3-4) upc-mr (2-8) (1-7) (5-8) lcc (4-9) (3-7) (1-4) utd (2-9) (2-8) (1-3) upc-jg (4-9) (7-9) (9) rali (4-9) (6-9) (6-8) upv (10) (10) (10) German-English (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) systran (1-4) (1-4) (7-9) uedin-phi (1-6) (1-7) (1) lcc (1-6) (1-7) (2-3) utd (2-7) (2-6) (4-6) ntt (1-9) (1-7) (3-5) nrc (3-8) (2-8) (7-8) upc-mr (4-8) (6-8) (4-6) upc-jmc (4-8) (3-9) (2-5) rali (8-9) (8-9) (8-9) upv (10) (10) (10) Figure 9: Evaluation of translation to English on out-of-domain test data 114 English-French (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) systran (1) (1) (1) upc-jmc (2-5) (2-4) (2-6) upc-mr (2-4) (2-4) (2-6) utd (2-6) (2-6) (7) rali (4-7) (5-7) (2-6) nrc (4-7) (4-7) (2-5) ntt (4-7) (4-7) (3-6) English-Spanish (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-mr (1-3) (1-6) (1-2) ms (1-7) (1-8) (6-7) utd (2-6) (1-7) (3-5) nrc (1-6) (2-7) (3-5) upc-jmc (2-7) (1-6) (3-5) ntt (2-7) (1-7) (1-2) rali (6-8) (4-8) (6-8) uedin-birch (6-10) (5-9) (7-8) upc-jg (8-9) (9-10) (9) upv (9) (8-9) (10) English-German (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) systran (1) (1-2) (1-6) upc-mr (2-3) (1-3) (1-5) upc-jmc (2-3) (3-6) (1-6) rali (4-6) (4-6) (1-6) nrc (4-6) (2-6) (2-6) ntt (4-6) (3-5) (1-6) upv (7) (7) (7) Figure 10: Evaluation of translation from English on out-of-domain test data 115 French-English In domain Out of Domain Adequacy Adequacy 0.3 0.3 • 0.2 0.2 0.1 0.1 -0.0 -0.0 -0.1 -0.1 -0.2 -0.2 -0.3 -0.3 -0.4 -0.4 -0.5 -0.5 -0.6 -0.6 -0.7 -0.7 •upv -0.8 -0.8 21 22 23 24 25 26 27 28 29 30 31 15 16 17 18 19 20 21 22 •upv •systran upcntt • rali upc-jmc • cc Fluency 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 •upv -0.5 •systran •upv upc -jmc • Fluency 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 • • • td t cc upc- • rali 21 22 23 24 25 26 27 28 29 30 31 15 16 17 18 19 20 21 22 Figure 11: Correlation between manual and automatic scores for French-English 116 Spanish-English Figure 12: Correlation between manual and automatic scores for Spanish-English -0.3 -0.4 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 •upv -0.4 •upv -0.3 In Domain •upc-jg Adequacy 0.3 0.2 0.1 -0.0 -0.1 -0.2 Out of Domain •upc-jmc •nrc •ntt Adequacy upc-jmc • • •lcc • rali • •rali -0.7 -0.5 -0.6 •upv 23 24 25 26 27 28 29 30 31 32 19 20 21 22 23 24 25 26 27 28 • •rali Fluency 0.2 0.1 -0.0 -0.1 -0.2 ntt • upc-mr •lcc •utd •upc-jg •rali Fluency 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 •upc-jmc • uedin-birch -0.5 -0.5 •upv 23 24 25 26 27 28 29 30 31 32 19 20 21 22 23 24 25 26 27 28 117 In Domain Out of Domain Adequacy Adequacy German-English 15 16 17 18 19 20 21 22 23 24 25 26 27 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 lcc • upc-jmc •systran •upv Fluency •ula •upc-mr •lcc 15 16 17 18 19 20 21 22 23 24 25 26 27 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 •systran •upv •uedin-phi -jmc •rali •systran -0.3 -0.4 -0.5 -0.6 •upv 12 13 14 15 16 17 18 19 20 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 Fluency uedin-phi • • •utd •upc-jmc •upc-mr 0.4 •rali -0.3 -0.4 -0.5 •upv 12 13 14 15 16 17 18 19 20 0.3 0.2 0.1 -0.0 -0.1 -0.2 English-French In Domain Out of Domain Adequacy Adequacy .

It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.

A non-optimal analysis is shown with dotted lines in the bottom frame.

A beam search concept is applied as in speech recognition.

Search CPU time mWER SSER Method [sec] [%] [%] MonS 0:9 42:0 30:5 QmS 10:6 34:4 23:8 IbmS 28:6 38:2 26:2 4.2 Performance Measures.

The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing.

pre-processing.

This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers.

For our experiments we also report the mean of precision and recall, which we denote by (P + R)I2 and F-measure.

The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.

J:j:l :zhongl :0.0 ;m,Jlong4 :0.0 (mHHaryg9tltHBI) £: _ADV: 5.98 ¥ :hua2:o.o E :_NC: 4.41 :mln2:o.o mm : guo2 : 0.0 (RopubllcofChlna) .....,.

They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs.

We incorporate instance weighting into a mixture-model framework, and find that it yields consistent improvements over a wide range of baselines.

The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing.

40 75.

Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.

9 50.2 +P RI OR be st me dia n 47.

This paper presents a maximum entropy-based named entity recognizer (NER).

On MUC6 data, MENERGI also achieves performance comparable to IdentiFinder when trained on similar amount of training data.

Combining multiple highly-accurate independent parsers yields promising results.

We call this approach parser switching.

However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.

The problem of coreference resolution has received considerable attention, including theoretical discourse models (e.g., (Grosz et al., 1995; Grosz and Sidner, 1998)), syntactic algorithms (e.g., (Hobbs, 1978; Lappin and Le- ass, 1994)), and supervised machine learning systems (Aone and Bennett, 1995; McCarthy and Lehnert, 1995; Ng and Cardie, 2002; Soon et al., 2001).

The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.

For the manual scoring, we can distinguish only half of the systems, both in terms of fluency and adequacy.

Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.

Language models are widely applied in natural language processing, and applications such as machine translation make very frequent queries.

This paper presents methods to query N-gram language models, minimizing time and space costs.

As noted in Section 4.4, disk cache state is controlled by reading the entire binary file before each test begins.

Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.

Thus in a two-hanzi word like lflli?J zhong1guo2 (middle country) 'China' there are two syllables, and at the same time two morphemes.

They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.

The dev corpus was taken from the NIST05 evaluation set, augmented with some randomly-selected material reserved from the training set.

Replacing this with a ranked evaluation seems to be more suitable.

The test set included 2000 sentences from the Europarl corpus, but also 1064 sentences out-ofdomain test data.

The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text.

constitute names, since we have only their segmentation, not the actual classification of the segmented words.

Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.

IRSTLM’s quantized variant is the inspiration for our quantized variant.

These clusters are computed using an SVD variant without relying on transitional structure.

The P (T |ψ) distribution, in English for instance, should have very low mass for the DT (determiner) tag, since determiners are a very small portion of the vocabulary.

The resulting model is compact, efficiently learnable and linguistically expressive.

Even without features, but still using the tag prior, our median result is 52.0%, still significantly outperforming Grac¸a et al.

This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration.

This FSA I can be segmented into words by composing Id(I) with D*, to form the WFST shown in Figure 2(c), then selecting the best path through this WFST to produce the WFST in Figure 2(d).

This paper conducted research in the area of automatic paraphrase discovery.

Finally, we find links between sets of phrases, based on the NE instance pair data (for example, different phrases which link “IBM” and “Lotus”) (Step 4).

From a DP-based solution to the traveling salesman problem, they present a novel technique to restrict the possible word reordering between source and target language in order to achieve an eÆcient search algorithm.

The translation direction is from German to English.

The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.

e0; e are the last two target words, C is a coverage set for the already covered source positions and j is the last position visited.

The approach assumes that the word reordering is restricted to a few positions in the source sentence.

This number must be less than or equal to n 􀀀 1.

This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.

Two annotators received training with the RST definitions and started the process with a first set of 10 texts, the results of which were intensively discussed and revised.

Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints.

Simple Type-Level Unsupervised POS Tagging

While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems.

In Figure 4, we displayed the number of system comparisons, for which we concluded statistical significance.

There is no global pruning.

The quasi-monotone search performs best in terms of both error rates mWER and SSER.

There is no global pruning.

vierten 12.

This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration.

Among these are words derived by various productive processes, including: 1.

Here we present two algorithms.

Having found (spelling, context) pairs in the parsed data, a number of features are extracted.

They showed better grammars to improve performance on both morphological and syntactic tasks, providing support for the advantage of a joint framework over pipelined or factorized ones.

3An English sentence with ambiguous PoS assignment can be trivially represented as a lattice similar to our own, where every pair of consecutive nodes correspond to a word, and every possible PoS assignment for this word is a connecting arc.

This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.

When the maSdar lacks a determiner, the constituent as a whole resem bles the ubiquitous annexation construct � ?f iDafa.

Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.

each word in the lexicon whether or not each string is actually an instance of the word in question.

The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.

The improvement is due to the cost of bit-level reads and avoiding reads that may fall in different virtual memory pages.

The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.

Formally, for a lexicon L and segments I ∈ L, O ∈/ L, each word automaton accepts the language I∗(O + I)I∗.

Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.

But we also need an estimate of the probability for a non-occurring though possible plural form like i¥JJ1l.f, nan2gua1-men0 'pumpkins.'

They showed the efficacy of graph-based label propagation for projecting part-of-speech information across languages.

Each feature concept is akin to a random variable and its occurrence in the text corresponds to a particular instantiation of that random variable.

Vijay-Shankar et all considered the structural descriptions produced by various grammatical formalisms in terms of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate.

The subtree under,; is excised from 7, the tree 7' is inserted in its place and the excised subtree is inserted below the foot of y'.

This assumption, however, is not inherent to type-based tagging models.

On several languages, we report performance exceeding that of more complex state-of-the art systems.1

Human judges also pointed out difficulties with the evaluation of long sentences.

It was our hope that this competition, which included the manual and automatic evaluation of statistical systems and one rulebased commercial system, will give further insight into the relation between automatic and manual evaluation.

Das and Petrov, in this paper, approached inducing unsupervised part-of-speech taggers for languages that had no labeled training data, but had translated text in a resource-rich language.

For unaligned words, we set the tag to the most frequent tag in the corresponding treebank.